Overview

Dataset statistics

Number of variables 71
Number of observations 20747
Missing cells 1349256
Missing cells (%) 91.6%
Duplicate rows 0
Duplicate rows (%) 0.0%
Total size in memory 11.2 MiB
Average record size in memory 568.0 B

Variable types

DateTime 1
Categorical 50
Numeric 8
Unsupported 12

Dataset

Description Returns the Point Of Interests surrounding the geocoordinates of where the phone is located. POI extracted every 5 minutes. To compare each sensor observation, the frequency was reduced to one minute. The first non-missing name is reported for each of the categorical variables.
Creator Matteo Busso, Massimo Stefan
Author Fausto Giunchiglia, Ivano Bison, Matteo Busso, Ronald Chenu-Abente, Marcelo Rodas Britez, Can Gunel, Giuseppe Veltri, Amalia de Götzen, Peter Kun, Amarsanaa Ganbold, Altangerel Chagnaa, George Gaskell, Miriam Bidoglia, Luca Cernuzzi, Alethia Hume, Jose Luis Zarza, Daniele Miorandi, Carlo Caprini
URL
Copyright (c) KnowDive 2022

Variable descriptions

timestamp show year(4), month(2), day(2), hour(2), minute(2), second(2), decimals(3)
experimentid Experiment Id
userid User id
bearing The compass direction from the current position the intended destination. Bearing is measured in degrees and calculated clockwise from true north (e.g., the bearing for the direction of east is 090°)
speed The speed of the device, measured in meters/second over ground
network_provider It indicates whether the coordinates were found using the network/Wi-Fi
gps_provider It indicates whether the coordinates were found using GPS
suburb The neighborhood where the POI is located
city The city where the POI is located
region The region where the POI is located
moving It indicates whether the user was moving
fclass0 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code0 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name0 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass1 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code1 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name1 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass2 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code2 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name2 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass3 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code3 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name3 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass4 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code4 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name4 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass5 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code5 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name5 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass6 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code6 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name6 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass7 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code7 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name7 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass8 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code8 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name8 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass9 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code9 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name9 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass10 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code10 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name10 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass11 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code11 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name11 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass12 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code12 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name12 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass13 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code13 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name13 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass14 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code14 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name14 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass15 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code15 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name15 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass16 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code16 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name16 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass17 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code17 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name17 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass18 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code18 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name18 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location
fclass19 Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location
code19 4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location
name19 Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Alerts

experimentid has constant value "wenet" Constant
userid is highly correlated with code3 and 9 other fields High correlation
bearing is highly correlated with speed and 10 other fields High correlation
speed is highly correlated with bearing and 10 other fields High correlation
network_provider is highly correlated with bearing and 7 other fields High correlation
gps_provider is highly correlated with bearing and 11 other fields High correlation
moving is highly correlated with bearing and 2 other fields High correlation
code0 is highly correlated with code2 and 10 other fields High correlation
code1 is highly correlated with code4 and 9 other fields High correlation
code2 is highly correlated with code0 and 10 other fields High correlation
code3 is highly correlated with userid and 14 other fields High correlation
code4 is highly correlated with code1 and 9 other fields High correlation
code5 is highly correlated with userid and 13 other fields High correlation
code6 is highly correlated with userid and 13 other fields High correlation
code7 is highly correlated with gps_provider and 8 other fields High correlation
code8 is highly correlated with gps_provider and 8 other fields High correlation
code9 is highly correlated with userid and 17 other fields High correlation
code10 is highly correlated with userid and 16 other fields High correlation
code11 is highly correlated with userid and 18 other fields High correlation
code12 is highly correlated with userid and 18 other fields High correlation
code13 is highly correlated with userid and 18 other fields High correlation
code14 is highly correlated with userid and 18 other fields High correlation
code15 is highly correlated with userid and 18 other fields High correlation
userid is highly correlated with code3 and 10 other fields High correlation
speed is highly correlated with code6 and 3 other fields High correlation
network_provider is highly correlated with gps_provider and 6 other fields High correlation
gps_provider is highly correlated with network_provider and 9 other fields High correlation
moving is highly correlated with network_provider and 1 other fields High correlation
code0 is highly correlated with code2 and 11 other fields High correlation
code1 is highly correlated with code6 and 8 other fields High correlation
code2 is highly correlated with code0 and 11 other fields High correlation
code3 is highly correlated with userid and 13 other fields High correlation
code4 is highly correlated with gps_provider and 8 other fields High correlation
code5 is highly correlated with userid and 11 other fields High correlation
code6 is highly correlated with userid and 14 other fields High correlation
code7 is highly correlated with userid and 16 other fields High correlation
code8 is highly correlated with gps_provider and 8 other fields High correlation
code9 is highly correlated with userid and 15 other fields High correlation
code10 is highly correlated with userid and 15 other fields High correlation
code11 is highly correlated with userid and 16 other fields High correlation
code12 is highly correlated with userid and 16 other fields High correlation
code13 is highly correlated with userid and 16 other fields High correlation
code14 is highly correlated with userid and 16 other fields High correlation
code15 is highly correlated with userid and 16 other fields High correlation
userid is highly correlated with code5 and 7 other fields High correlation
bearing is highly correlated with speed and 9 other fields High correlation
speed is highly correlated with bearing and 9 other fields High correlation
network_provider is highly correlated with bearing and 7 other fields High correlation
gps_provider is highly correlated with bearing and 11 other fields High correlation
moving is highly correlated with bearing and 2 other fields High correlation
code0 is highly correlated with code2 and 10 other fields High correlation
code1 is highly correlated with code6 and 7 other fields High correlation
code2 is highly correlated with code0 and 9 other fields High correlation
code3 is highly correlated with network_provider and 10 other fields High correlation
code4 is highly correlated with code9 and 6 other fields High correlation
code5 is highly correlated with userid and 11 other fields High correlation
code6 is highly correlated with network_provider and 9 other fields High correlation
code7 is highly correlated with gps_provider and 8 other fields High correlation
code8 is highly correlated with gps_provider and 8 other fields High correlation
code9 is highly correlated with userid and 17 other fields High correlation
code10 is highly correlated with userid and 16 other fields High correlation
code11 is highly correlated with userid and 18 other fields High correlation
code12 is highly correlated with userid and 18 other fields High correlation
code13 is highly correlated with userid and 18 other fields High correlation
code14 is highly correlated with userid and 18 other fields High correlation
code15 is highly correlated with userid and 18 other fields High correlation
userid is highly correlated with network_provider and 53 other fields High correlation
bearing is highly correlated with speed and 14 other fields High correlation
speed is highly correlated with bearing and 1 other fields High correlation
network_provider is highly correlated with userid and 34 other fields High correlation
gps_provider is highly correlated with userid and 43 other fields High correlation
suburb is highly correlated with userid and 55 other fields High correlation
city is highly correlated with userid and 52 other fields High correlation
region is highly correlated with userid and 51 other fields High correlation
moving is highly correlated with userid and 4 other fields High correlation
fclass0 is highly correlated with userid and 50 other fields High correlation
code0 is highly correlated with userid and 50 other fields High correlation
name0 is highly correlated with userid and 32 other fields High correlation
fclass1 is highly correlated with userid and 53 other fields High correlation
code1 is highly correlated with userid and 51 other fields High correlation
name1 is highly correlated with userid and 24 other fields High correlation
fclass2 is highly correlated with userid and 52 other fields High correlation
code2 is highly correlated with userid and 47 other fields High correlation
name2 is highly correlated with userid and 52 other fields High correlation
fclass3 is highly correlated with userid and 52 other fields High correlation
code3 is highly correlated with userid and 51 other fields High correlation
name3 is highly correlated with userid and 49 other fields High correlation
fclass4 is highly correlated with userid and 52 other fields High correlation
code4 is highly correlated with userid and 21 other fields High correlation
name4 is highly correlated with userid and 33 other fields High correlation
fclass5 is highly correlated with userid and 52 other fields High correlation
code5 is highly correlated with userid and 33 other fields High correlation
name5 is highly correlated with userid and 19 other fields High correlation
fclass6 is highly correlated with userid and 53 other fields High correlation
code6 is highly correlated with userid and 53 other fields High correlation
name6 is highly correlated with userid and 31 other fields High correlation
fclass7 is highly correlated with userid and 50 other fields High correlation
code7 is highly correlated with userid and 50 other fields High correlation
name7 is highly correlated with userid and 29 other fields High correlation
fclass8 is highly correlated with userid and 50 other fields High correlation
code8 is highly correlated with userid and 50 other fields High correlation
name8 is highly correlated with userid and 27 other fields High correlation
fclass9 is highly correlated with userid and 43 other fields High correlation
code9 is highly correlated with userid and 43 other fields High correlation
name9 is highly correlated with userid and 50 other fields High correlation
fclass10 is highly correlated with userid and 50 other fields High correlation
code10 is highly correlated with userid and 50 other fields High correlation
name10 is highly correlated with userid and 50 other fields High correlation
fclass11 is highly correlated with userid and 42 other fields High correlation
code11 is highly correlated with userid and 42 other fields High correlation
name11 is highly correlated with userid and 42 other fields High correlation
fclass12 is highly correlated with userid and 42 other fields High correlation
code12 is highly correlated with userid and 42 other fields High correlation
name12 is highly correlated with userid and 42 other fields High correlation
fclass13 is highly correlated with userid and 42 other fields High correlation
code13 is highly correlated with userid and 42 other fields High correlation
name13 is highly correlated with userid and 42 other fields High correlation
fclass14 is highly correlated with userid and 42 other fields High correlation
code14 is highly correlated with userid and 42 other fields High correlation
name14 is highly correlated with userid and 42 other fields High correlation
fclass15 is highly correlated with userid and 42 other fields High correlation
code15 is highly correlated with userid and 42 other fields High correlation
name15 is highly correlated with userid and 42 other fields High correlation
experimentid has 13372 (64.5%) missing values Missing
userid has 13372 (64.5%) missing values Missing
bearing has 13372 (64.5%) missing values Missing
speed has 13372 (64.5%) missing values Missing
network_provider has 13372 (64.5%) missing values Missing
gps_provider has 13372 (64.5%) missing values Missing
suburb has 15321 (73.8%) missing values Missing
city has 13372 (64.5%) missing values Missing
region has 13372 (64.5%) missing values Missing
moving has 13372 (64.5%) missing values Missing
fclass0 has 14101 (68.0%) missing values Missing
code0 has 14101 (68.0%) missing values Missing
name0 has 15875 (76.5%) missing values Missing
fclass1 has 19525 (94.1%) missing values Missing
code1 has 19525 (94.1%) missing values Missing
name1 has 19773 (95.3%) missing values Missing
fclass2 has 19740 (95.1%) missing values Missing
code2 has 19740 (95.1%) missing values Missing
name2 has 20130 (97.0%) missing values Missing
fclass3 has 20324 (98.0%) missing values Missing
code3 has 20324 (98.0%) missing values Missing
name3 has 20565 (99.1%) missing values Missing
fclass4 has 20324 (98.0%) missing values Missing
code4 has 20324 (98.0%) missing values Missing
name4 has 20350 (98.1%) missing values Missing
fclass5 has 20398 (98.3%) missing values Missing
code5 has 20398 (98.3%) missing values Missing
name5 has 20528 (98.9%) missing values Missing
fclass6 has 20493 (98.8%) missing values Missing
code6 has 20493 (98.8%) missing values Missing
name6 has 20519 (98.9%) missing values Missing
fclass7 has 20517 (98.9%) missing values Missing
code7 has 20517 (98.9%) missing values Missing
name7 has 20530 (99.0%) missing values Missing
fclass8 has 20517 (98.9%) missing values Missing
code8 has 20517 (98.9%) missing values Missing
name8 has 20528 (98.9%) missing values Missing
fclass9 has 20517 (98.9%) missing values Missing
code9 has 20517 (98.9%) missing values Missing
name9 has 20517 (98.9%) missing values Missing
fclass10 has 20517 (98.9%) missing values Missing
code10 has 20517 (98.9%) missing values Missing
name10 has 20517 (98.9%) missing values Missing
fclass11 has 20723 (99.9%) missing values Missing
code11 has 20723 (99.9%) missing values Missing
name11 has 20723 (99.9%) missing values Missing
fclass12 has 20723 (99.9%) missing values Missing
code12 has 20723 (99.9%) missing values Missing
name12 has 20723 (99.9%) missing values Missing
fclass13 has 20723 (99.9%) missing values Missing
code13 has 20723 (99.9%) missing values Missing
name13 has 20723 (99.9%) missing values Missing
fclass14 has 20723 (99.9%) missing values Missing
code14 has 20723 (99.9%) missing values Missing
name14 has 20723 (99.9%) missing values Missing
fclass15 has 20723 (99.9%) missing values Missing
code15 has 20723 (99.9%) missing values Missing
name15 has 20723 (99.9%) missing values Missing
fclass16 has 20747 (100.0%) missing values Missing
code16 has 20747 (100.0%) missing values Missing
name16 has 20747 (100.0%) missing values Missing
fclass17 has 20747 (100.0%) missing values Missing
code17 has 20747 (100.0%) missing values Missing
name17 has 20747 (100.0%) missing values Missing
fclass18 has 20747 (100.0%) missing values Missing
code18 has 20747 (100.0%) missing values Missing
name18 has 20747 (100.0%) missing values Missing
fclass19 has 20747 (100.0%) missing values Missing
code19 has 20747 (100.0%) missing values Missing
name19 has 20747 (100.0%) missing values Missing
timestamp has unique values Unique
fclass16 is an unsupported type, check if it needs cleaning or further analysis Unsupported
code16 is an unsupported type, check if it needs cleaning or further analysis Unsupported
name16 is an unsupported type, check if it needs cleaning or further analysis Unsupported
fclass17 is an unsupported type, check if it needs cleaning or further analysis Unsupported
code17 is an unsupported type, check if it needs cleaning or further analysis Unsupported
name17 is an unsupported type, check if it needs cleaning or further analysis Unsupported
fclass18 is an unsupported type, check if it needs cleaning or further analysis Unsupported
code18 is an unsupported type, check if it needs cleaning or further analysis Unsupported
name18 is an unsupported type, check if it needs cleaning or further analysis Unsupported
fclass19 is an unsupported type, check if it needs cleaning or further analysis Unsupported
code19 is an unsupported type, check if it needs cleaning or further analysis Unsupported
name19 is an unsupported type, check if it needs cleaning or further analysis Unsupported
userid has 1343 (6.5%) zeros Zeros
bearing has 547 (2.6%) zeros Zeros
speed has 550 (2.7%) zeros Zeros

Reproduction

Analysis started 2022-07-04 18:05:06.591849
Analysis finished 2022-07-04 18:05:42.481066
Duration 35.89 seconds
Software version pandas-profiling v3.2.0
Download configuration config.json

Variables

timestamp
Date

UNIQUE

show year(4), month(2), day(2), hour(2), minute(2), second(2), decimals(3)

Distinct 20747
Distinct (%) 100.0%
Missing 0
Missing (%) 0.0%
Memory size 162.2 KiB
Minimum 2021-11-22 03:20:00
Maximum 2021-12-06 13:06:00
2022-07-04T20:05:42.637029 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:42.948462 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

experimentid
Categorical

CONSTANT
MISSING
REJECTED

Experiment Id

Distinct 1
Distinct (%) < 0.1%
Missing 13372
Missing (%) 64.5%
Memory size 162.2 KiB
wenet
7375

Length

Max length 5
Median length 5
Mean length 5
Min length 5

Characters and Unicode

Total characters 36875
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row wenet
2nd row wenet
3rd row wenet
4th row wenet
5th row wenet

Common Values

Value Count Frequency (%)
wenet 7375
35.5%
(Missing) 13372
64.5%

Length

2022-07-04T20:05:43.216255 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:43.424567 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
wenet 7375
100.0%

Most occurring characters

Value Count Frequency (%)
e 14750
40.0%
w 7375
20.0%
n 7375
20.0%
t 7375
20.0%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 36875
100.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 14750
40.0%
w 7375
20.0%
n 7375
20.0%
t 7375
20.0%

Most occurring scripts

Value Count Frequency (%)
Latin 36875
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 14750
40.0%
w 7375
20.0%
n 7375
20.0%
t 7375
20.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 36875
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 14750
40.0%
w 7375
20.0%
n 7375
20.0%
t 7375
20.0%

userid
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

User id

Distinct 6
Distinct (%) 0.1%
Missing 13372
Missing (%) 64.5%
Infinite 0
Infinite (%) 0.0%
Mean 5.691389831
Minimum 0
Maximum 13
Zeros 1343
Zeros (%) 6.5%
Negative 0
Negative (%) 0.0%
Memory size 162.2 KiB
2022-07-04T20:05:43.576404 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 4
median 4
Q3 12
95-th percentile 12
Maximum 13
Range 13
Interquartile range (IQR) 8

Descriptive statistics

Standard deviation 4.547038952
Coefficient of variation (CV) 0.7989329649
Kurtosis -1.291218136
Mean 5.691389831
Median Absolute Deviation (MAD) 3
Skewness 0.443609491
Sum 41974
Variance 20.67556323
Monotonicity Not monotonic
2022-07-04T20:05:43.779223 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
Value Count Frequency (%)
4 3472
16.7%
12 2064
9.9%
0 1343
6.5%
1 249
1.2%
13 176
0.8%
11 71
0.3%
(Missing) 13372
64.5%
Value Count Frequency (%)
0 1343
6.5%
1 249
1.2%
4 3472
16.7%
11 71
0.3%
12 2064
9.9%
13 176
0.8%
Value Count Frequency (%)
13 176
0.8%
12 2064
9.9%
11 71
0.3%
4 3472
16.7%
1 249
1.2%
0 1343
6.5%

bearing
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

The compass direction from the current position the intended destination. Bearing is measured in degrees and calculated clockwise from true north (e.g., the bearing for the direction of east is 090°)

Distinct 402
Distinct (%) 5.5%
Missing 13372
Missing (%) 64.5%
Infinite 0
Infinite (%) 0.0%
Mean 4.413579137
Minimum -1
Maximum 354.34
Zeros 547
Zeros (%) 2.6%
Negative 6476
Negative (%) 31.2%
Memory size 162.2 KiB
2022-07-04T20:05:44.030823 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum -1
5-th percentile -1
Q1 -1
median -1
Q3 -1
95-th percentile 0
Maximum 354.34
Range 355.34
Interquartile range (IQR) 0

Descriptive statistics

Standard deviation 29.33182161
Coefficient of variation (CV) 6.645813
Kurtosis 50.79748293
Mean 4.413579137
Median Absolute Deviation (MAD) 0
Skewness 6.740978294
Sum 32550.14614
Variance 860.3557587
Monotonicity Not monotonic
2022-07-04T20:05:44.316379 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
-1 6329
30.5%
0 547
2.6%
-0.3333333333 17
0.1%
-0.2 15
0.1%
-0.1428571429 15
0.1%
-0.1780821918 10
< 0.1%
-0.1111111111 9
< 0.1%
-0.5 8
< 0.1%
-0.1705069124 8
< 0.1%
-0.1666666667 6
< 0.1%
Other values (392) 411
2.0%
(Missing) 13372
64.5%
Value Count Frequency (%)
-1 6329
30.5%
-0.6 1
< 0.1%
-0.5285714286 1
< 0.1%
-0.5 8
< 0.1%
-0.3636363636 1
< 0.1%
-0.3333333333 17
0.1%
-0.2571428571 1
< 0.1%
-0.25 2
< 0.1%
-0.2419354839 1
< 0.1%
-0.2388059701 1
< 0.1%
Value Count Frequency (%)
354.34 1
< 0.1%
346.7 1
< 0.1%
335 1
< 0.1%
332.8 1
< 0.1%
330.05 1
< 0.1%
313 1
< 0.1%
312 1
< 0.1%
305.2 1
< 0.1%
296.8984615 1
< 0.1%
286.2 1
< 0.1%

speed
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

The speed of the device, measured in meters/second over ground

Distinct 432
Distinct (%) 5.9%
Missing 13372
Missing (%) 64.5%
Infinite 0
Infinite (%) 0.0%
Mean -0.3000646953
Minimum -1
Maximum 23.622
Zeros 550
Zeros (%) 2.7%
Negative 6509
Negative (%) 31.4%
Memory size 162.2 KiB
2022-07-04T20:05:44.623317 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum -1
5-th percentile -1
Q1 -0.505
median -0.2575
Q3 -0.2575
95-th percentile 0
Maximum 23.622
Range 24.622
Interquartile range (IQR) 0.2475

Descriptive statistics

Standard deviation 1.027595029
Coefficient of variation (CV) -3.42457825
Kurtosis 200.2090198
Mean -0.3000646953
Median Absolute Deviation (MAD) 0.2475
Skewness 12.25821453
Sum -2212.977128
Variance 1.055951544
Monotonicity Not monotonic
2022-07-04T20:05:44.901657 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
-0.505 2326
11.2%
-0.2575 2178
10.5%
-1 1136
5.5%
0 550
2.7%
-0.01 382
1.8%
-0.1514285714 58
0.3%
-0.34 48
0.2%
-0.01 35
0.2%
-0.2575 25
0.1%
-0.505 21
0.1%
Other values (422) 616
3.0%
(Missing) 13372
64.5%
Value Count Frequency (%)
-1 1136
5.5%
-0.505 2326
11.2%
-0.505 21
0.1%
-0.4342857143 1
< 0.1%
-0.4342857143 1
< 0.1%
-0.406 3
< 0.1%
-0.406 8
< 0.1%
-0.38125 2
< 0.1%
-0.38125 3
< 0.1%
-0.34 48
0.2%
Value Count Frequency (%)
23.622 1
< 0.1%
21.97375 1
< 0.1%
21.4 1
< 0.1%
19.4475 1
< 0.1%
16.64 1
< 0.1%
16.63295082 1
< 0.1%
16.37285714 1
< 0.1%
16.11377049 1
< 0.1%
15.15833333 1
< 0.1%
14.78333333 1
< 0.1%

network_provider
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

It indicates whether the coordinates were found using the network/Wi-Fi

Distinct 2
Distinct (%) < 0.1%
Missing 13372
Missing (%) 64.5%
Memory size 162.2 KiB
1.0
6028
0.0
1347

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 22125
Distinct characters 3
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 1.0
2nd row 1.0
3rd row 1.0
4th row 1.0
5th row 1.0

Common Values

Value Count Frequency (%)
1.0 6028
29.1%
0.0 1347
6.5%
(Missing) 13372
64.5%

Length

2022-07-04T20:05:45.149454 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:45.377475 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
1.0 6028
81.7%
0.0 1347
18.3%

Most occurring characters

Value Count Frequency (%)
0 8722
39.4%
. 7375
33.3%
1 6028
27.2%

Most occurring categories

Value Count Frequency (%)
Decimal Number 14750
66.7%
Other Punctuation 7375
33.3%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 8722
59.1%
1 6028
40.9%
Other Punctuation
Value Count Frequency (%)
. 7375
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 22125
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 8722
39.4%
. 7375
33.3%
1 6028
27.2%

Most occurring blocks

Value Count Frequency (%)
ASCII 22125
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 8722
39.4%
. 7375
33.3%
1 6028
27.2%

gps_provider
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

It indicates whether the coordinates were found using GPS

Distinct 2
Distinct (%) < 0.1%
Missing 13372
Missing (%) 64.5%
Memory size 162.2 KiB
0.0
6504
1.0
871

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 22125
Distinct characters 3
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 6504
31.3%
1.0 871
4.2%
(Missing) 13372
64.5%

Length

2022-07-04T20:05:45.570464 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:45.793600 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.0 6504
88.2%
1.0 871
11.8%

Most occurring characters

Value Count Frequency (%)
0 13879
62.7%
. 7375
33.3%
1 871
3.9%

Most occurring categories

Value Count Frequency (%)
Decimal Number 14750
66.7%
Other Punctuation 7375
33.3%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 13879
94.1%
1 871
5.9%
Other Punctuation
Value Count Frequency (%)
. 7375
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 22125
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 13879
62.7%
. 7375
33.3%
1 871
3.9%

Most occurring blocks

Value Count Frequency (%)
ASCII 22125
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 13879
62.7%
. 7375
33.3%
1 871
3.9%

suburb
Categorical

HIGH CORRELATION
MISSING

The neighborhood where the POI is located

Distinct 39
Distinct (%) 0.7%
Missing 15321
Missing (%) 73.8%
Memory size 162.2 KiB
General Díaz
3294
Madame Lynch
1313
Centro
249
8th district
155
Manorá
68
Other values (34)
347

Length

Max length 23
Median length 12
Mean length 11.62845558
Min length 6

Characters and Unicode

Total characters 63096
Distinct characters 53
Distinct categories 5 ?
Distinct scripts 2 ?
Distinct blocks 2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 5 ?
Unique (%) 0.1%

Sample

1st row General Díaz
2nd row General Díaz
3rd row General Díaz
4th row General Díaz
5th row General Díaz

Common Values

Value Count Frequency (%)
General Díaz 3294
15.9%
Madame Lynch 1313
6.3%
Centro 249
1.2%
8th district 155
0.7%
Manorá 68
0.3%
Sankt Georgen 59
0.3%
Rotmonten 36
0.2%
King's Cross 34
0.2%
Primer Barrio 34
0.2%
San Jorge 22
0.1%
Other values (29) 162
0.8%
(Missing) 15321
73.8%

Length

2022-07-04T20:05:46.007663 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
general 3294
31.5%
díaz 3294
31.5%
madame 1313
12.5%
lynch 1313
12.5%
centro 249
2.4%
district 169
1.6%
8th 155
1.5%
manorá 68
0.6%
sankt 62
0.6%
georgen 59
0.6%
Other values (54) 497
4.7%

Most occurring characters

Value Count Frequency (%)
a 9593
15.2%
e 8500
13.5%
n 5267
8.3%
5047
8.0%
r 4195
6.6%
G 3364
5.3%
l 3356
5.3%
z 3306
5.2%
D 3299
5.2%
í 3297
5.2%
Other values (43) 13872
22.0%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 47714
75.6%
Uppercase Letter 10132
16.1%
Space Separator 5047
8.0%
Decimal Number 169
0.3%
Other Punctuation 34
0.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 9593
20.1%
e 8500
17.8%
n 5267
11.0%
r 4195
8.8%
l 3356
7.0%
z 3306
6.9%
í 3297
6.9%
d 1516
3.2%
c 1509
3.2%
h 1499
3.1%
Other values (18) 5676
11.9%
Uppercase Letter
Value Count Frequency (%)
G 3364
33.2%
D 3299
32.6%
M 1390
13.7%
L 1336
13.2%
C 317
3.1%
S 135
1.3%
R 82
0.8%
B 68
0.7%
P 45
0.4%
K 34
0.3%
Other values (10) 62
0.6%
Decimal Number
Value Count Frequency (%)
8 155
91.7%
6 12
7.1%
5 2
1.2%
Space Separator
Value Count Frequency (%)
5047
100.0%
Other Punctuation
Value Count Frequency (%)
' 34
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 57846
91.7%
Common 5250
8.3%

Most frequent character per script

Latin
Value Count Frequency (%)
a 9593
16.6%
e 8500
14.7%
n 5267
9.1%
r 4195
7.3%
G 3364
5.8%
l 3356
5.8%
z 3306
5.7%
D 3299
5.7%
í 3297
5.7%
d 1516
2.6%
Other values (38) 12153
21.0%
Common
Value Count Frequency (%)
5047
96.1%
8 155
3.0%
' 34
0.6%
6 12
0.2%
5 2
< 0.1%

Most occurring blocks

Value Count Frequency (%)
ASCII 59688
94.6%
None 3408
5.4%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 9593
16.1%
e 8500
14.2%
n 5267
8.8%
5047
8.5%
r 4195
7.0%
G 3364
5.6%
l 3356
5.6%
z 3306
5.5%
D 3299
5.5%
d 1516
2.5%
Other values (38) 12245
20.5%
None
Value Count Frequency (%)
í 3297
96.7%
á 78
2.3%
ü 14
0.4%
ó 12
0.4%
é 7
0.2%

city
Categorical

HIGH CORRELATION
MISSING

The city where the POI is located

Distinct 9
Distinct (%) 0.1%
Missing 13372
Missing (%) 64.5%
Memory size 162.2 KiB
Asuncion
4756
Herisau
1943
Comarca de la Vega de Granada
249
Budapest
176
St. Gallen
121
Other values (4)
130

Length

Max length 29
Median length 8
Mean length 8.49979661
Min length 5

Characters and Unicode

Total characters 62686
Distinct characters 29
Distinct categories 4 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Asuncion
2nd row Asuncion
3rd row Asuncion
4th row Asuncion
5th row Asuncion

Common Values

Value Count Frequency (%)
Asuncion 4756
22.9%
Herisau 1943
9.4%
Comarca de la Vega de Granada 249
1.2%
Budapest 176
0.8%
St. Gallen 121
0.6%
Luque 59
0.3%
London 48
0.2%
London Borough of Islington 21
0.1%
London Borough of Camden 2
< 0.1%
(Missing) 13372
64.5%

Length

2022-07-04T20:05:46.246385 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:46.511555 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
asuncion 4756
54.0%
herisau 1943
22.1%
de 498
5.7%
comarca 249
2.8%
la 249
2.8%
vega 249
2.8%
granada 249
2.8%
budapest 176
2.0%
st 121
1.4%
gallen 121
1.4%
Other values (6) 199
2.3%

Most occurring characters

Value Count Frequency (%)
n 10068
16.1%
u 7016
11.2%
s 6896
11.0%
i 6720
10.7%
o 5237
8.4%
c 5005
8.0%
A 4756
7.6%
a 3985
6.4%
e 3048
4.9%
r 2464
3.9%
Other values (19) 7491
12.0%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 53090
84.7%
Uppercase Letter 8040
12.8%
Space Separator 1435
2.3%
Other Punctuation 121
0.2%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
n 10068
19.0%
u 7016
13.2%
s 6896
13.0%
i 6720
12.7%
o 5237
9.9%
c 5005
9.4%
a 3985
7.5%
e 3048
5.7%
r 2464
4.6%
d 996
1.9%
Other values (8) 1655
3.1%
Uppercase Letter
Value Count Frequency (%)
A 4756
59.2%
H 1943
24.2%
G 370
4.6%
C 251
3.1%
V 249
3.1%
B 199
2.5%
L 130
1.6%
S 121
1.5%
I 21
0.3%
Space Separator
Value Count Frequency (%)
1435
100.0%
Other Punctuation
Value Count Frequency (%)
. 121
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 61130
97.5%
Common 1556
2.5%

Most frequent character per script

Latin
Value Count Frequency (%)
n 10068
16.5%
u 7016
11.5%
s 6896
11.3%
i 6720
11.0%
o 5237
8.6%
c 5005
8.2%
A 4756
7.8%
a 3985
6.5%
e 3048
5.0%
r 2464
4.0%
Other values (17) 5935
9.7%
Common
Value Count Frequency (%)
1435
92.2%
. 121
7.8%

Most occurring blocks

Value Count Frequency (%)
ASCII 62686
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
n 10068
16.1%
u 7016
11.2%
s 6896
11.0%
i 6720
10.7%
o 5237
8.4%
c 5005
8.0%
A 4756
7.6%
a 3985
6.4%
e 3048
4.9%
r 2464
3.9%
Other values (19) 7491
12.0%

region
Categorical

HIGH CORRELATION
MISSING

The region where the POI is located

Distinct 7
Distinct (%) 0.1%
Missing 13372
Missing (%) 64.5%
Memory size 162.2 KiB
Distrito Capital de Paraguay
4756
Appenzell Ausserrhoden
1943
Andalusia
249
Central Hungary
176
St. Gallen
121
Other values (2)
130

Length

Max length 28
Median length 28
Mean length 24.8660339
Min length 7

Characters and Unicode

Total characters 183387
Distinct characters 29
Distinct categories 4 ?
Distinct scripts 2 ?
Distinct blocks 2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Distrito Capital de Paraguay
2nd row Distrito Capital de Paraguay
3rd row Distrito Capital de Paraguay
4th row Distrito Capital de Paraguay
5th row Distrito Capital de Paraguay

Common Values

Value Count Frequency (%)
Distrito Capital de Paraguay 4756
22.9%
Appenzell Ausserrhoden 1943
9.4%
Andalusia 249
1.2%
Central Hungary 176
0.8%
St. Gallen 121
0.6%
England 71
0.3%
Región Oriental 59
0.3%
(Missing) 13372
64.5%

Length

2022-07-04T20:05:46.776861 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:47.018158 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
distrito 4756
19.9%
capital 4756
19.9%
de 4756
19.9%
paraguay 4756
19.9%
appenzell 1943
8.1%
ausserrhoden 1943
8.1%
andalusia 249
1.0%
central 176
0.7%
hungary 176
0.7%
st 121
0.5%
Other values (4) 310
1.3%

Most occurring characters

Value Count Frequency (%)
a 24881
13.6%
16567
9.0%
i 14635
8.0%
t 14624
8.0%
r 13809
7.5%
e 12943
7.1%
l 9439
5.1%
s 8891
4.8%
p 8642
4.7%
u 7124
3.9%
Other values (19) 51832
28.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 147513
80.4%
Uppercase Letter 19186
10.5%
Space Separator 16567
9.0%
Other Punctuation 121
0.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 24881
16.9%
i 14635
9.9%
t 14624
9.9%
r 13809
9.4%
e 12943
8.8%
l 9439
6.4%
s 8891
6.0%
p 8642
5.9%
u 7124
4.8%
d 7019
4.8%
Other values (7) 25506
17.3%
Uppercase Letter
Value Count Frequency (%)
C 4932
25.7%
D 4756
24.8%
P 4756
24.8%
A 4135
21.6%
H 176
0.9%
S 121
0.6%
G 121
0.6%
E 71
0.4%
R 59
0.3%
O 59
0.3%
Space Separator
Value Count Frequency (%)
16567
100.0%
Other Punctuation
Value Count Frequency (%)
. 121
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 166699
90.9%
Common 16688
9.1%

Most frequent character per script

Latin
Value Count Frequency (%)
a 24881
14.9%
i 14635
8.8%
t 14624
8.8%
r 13809
8.3%
e 12943
7.8%
l 9439
5.7%
s 8891
5.3%
p 8642
5.2%
u 7124
4.3%
d 7019
4.2%
Other values (17) 44692
26.8%
Common
Value Count Frequency (%)
16567
99.3%
. 121
0.7%

Most occurring blocks

Value Count Frequency (%)
ASCII 183328
> 99.9%
None 59
< 0.1%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 24881
13.6%
16567
9.0%
i 14635
8.0%
t 14624
8.0%
r 13809
7.5%
e 12943
7.1%
l 9439
5.1%
s 8891
4.8%
p 8642
4.7%
u 7124
3.9%
Other values (18) 51773
28.2%
None
Value Count Frequency (%)
ó 59
100.0%

moving
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

It indicates whether the user was moving

Distinct 2
Distinct (%) < 0.1%
Missing 13372
Missing (%) 64.5%
Memory size 162.2 KiB
0.0
6548
1.0
827

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 22125
Distinct characters 3
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 6548
31.6%
1.0 827
4.0%
(Missing) 13372
64.5%

Length

2022-07-04T20:05:47.271867 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:47.490945 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
0.0 6548
88.8%
1.0 827
11.2%

Most occurring characters

Value Count Frequency (%)
0 13923
62.9%
. 7375
33.3%
1 827
3.7%

Most occurring categories

Value Count Frequency (%)
Decimal Number 14750
66.7%
Other Punctuation 7375
33.3%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 13923
94.4%
1 827
5.6%
Other Punctuation
Value Count Frequency (%)
. 7375
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 22125
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 13923
62.9%
. 7375
33.3%
1 827
3.7%

Most occurring blocks

Value Count Frequency (%)
ASCII 22125
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 13923
62.9%
. 7375
33.3%
1 827
3.7%

fclass0
Categorical

HIGH CORRELATION
MISSING

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Distinct 5
Distinct (%) 0.1%
Missing 14101
Missing (%) 68.0%
Memory size 162.2 KiB
residential
6126
industrial
256
restaurant
248
retail
13
parking
3

Length

Max length 11
Median length 11
Mean length 10.91257899
Min length 6

Characters and Unicode

Total characters 72525
Distinct characters 13
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row residential
2nd row residential
3rd row residential
4th row residential
5th row residential

Common Values

Value Count Frequency (%)
residential 6126
29.5%
industrial 256
1.2%
restaurant 248
1.2%
retail 13
0.1%
parking 3
< 0.1%
(Missing) 14101
68.0%

Length

2022-07-04T20:05:47.984876 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:48.219455 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
residential 6126
92.2%
industrial 256
3.9%
restaurant 248
3.7%
retail 13
0.2%
parking 3
< 0.1%

Most occurring characters

Value Count Frequency (%)
i 12780
17.6%
e 12513
17.3%
r 6894
9.5%
a 6894
9.5%
t 6891
9.5%
n 6633
9.1%
s 6630
9.1%
l 6395
8.8%
d 6382
8.8%
u 504
0.7%
Other values (3) 9
< 0.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 72525
100.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
i 12780
17.6%
e 12513
17.3%
r 6894
9.5%
a 6894
9.5%
t 6891
9.5%
n 6633
9.1%
s 6630
9.1%
l 6395
8.8%
d 6382
8.8%
u 504
0.7%
Other values (3) 9
< 0.1%

Most occurring scripts

Value Count Frequency (%)
Latin 72525
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
i 12780
17.6%
e 12513
17.3%
r 6894
9.5%
a 6894
9.5%
t 6891
9.5%
n 6633
9.1%
s 6630
9.1%
l 6395
8.8%
d 6382
8.8%
u 504
0.7%
Other values (3) 9
< 0.1%

Most occurring blocks

Value Count Frequency (%)
ASCII 72525
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
i 12780
17.6%
e 12513
17.3%
r 6894
9.5%
a 6894
9.5%
t 6891
9.5%
n 6633
9.1%
s 6630
9.1%
l 6395
8.8%
d 6382
8.8%
u 504
0.7%
Other values (3) 9
< 0.1%

code0
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Distinct 5
Distinct (%) 0.1%
Missing 14101
Missing (%) 68.0%
Memory size 162.2 KiB
7203.0
6126
7204.0
256
2301.0
248
7212.0
13
5260.0
3

Length

Max length 6
Median length 6
Mean length 6
Min length 6

Characters and Unicode

Total characters 39876
Distinct characters 9
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 7203.0
2nd row 7203.0
3rd row 7203.0
4th row 7203.0
5th row 7203.0

Common Values

Value Count Frequency (%)
7203.0 6126
29.5%
7204.0 256
1.2%
2301.0 248
1.2%
7212.0 13
0.1%
5260.0 3
< 0.1%
(Missing) 14101
68.0%

Length

2022-07-04T20:05:48.429508 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:48.661556 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
7203.0 6126
92.2%
7204.0 256
3.9%
2301.0 248
3.7%
7212.0 13
0.2%
5260.0 3
< 0.1%

Most occurring characters

Value Count Frequency (%)
0 13279
33.3%
2 6659
16.7%
. 6646
16.7%
7 6395
16.0%
3 6374
16.0%
1 261
0.7%
4 256
0.6%
5 3
< 0.1%
6 3
< 0.1%

Most occurring categories

Value Count Frequency (%)
Decimal Number 33230
83.3%
Other Punctuation 6646
16.7%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 13279
40.0%
2 6659
20.0%
7 6395
19.2%
3 6374
19.2%
1 261
0.8%
4 256
0.8%
5 3
< 0.1%
6 3
< 0.1%
Other Punctuation
Value Count Frequency (%)
. 6646
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 39876
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 13279
33.3%
2 6659
16.7%
. 6646
16.7%
7 6395
16.0%
3 6374
16.0%
1 261
0.7%
4 256
0.6%
5 3
< 0.1%
6 3
< 0.1%

Most occurring blocks

Value Count Frequency (%)
ASCII 39876
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 13279
33.3%
2 6659
16.7%
. 6646
16.7%
7 6395
16.0%
3 6374
16.0%
1 261
0.7%
4 256
0.6%
5 3
< 0.1%
6 3
< 0.1%

name0
Categorical

HIGH CORRELATION
MISSING

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Distinct 7
Distinct (%) 0.1%
Missing 15875
Missing (%) 76.5%
Memory size 162.2 KiB
General Díaz
3263
Madame Lynch
1255
Puerta de la Alpujarra
248
Manorá
52
Primer Barrio
31
Other values (2)
23

Length

Max length 22
Median length 12
Mean length 12.45053366
Min length 6

Characters and Unicode

Total characters 60659
Distinct characters 32
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row General Díaz
2nd row General Díaz
3rd row General Díaz
4th row General Díaz
5th row General Díaz

Common Values

Value Count Frequency (%)
General Díaz 3263
15.7%
Madame Lynch 1255
6.0%
Puerta de la Alpujarra 248
1.2%
Manorá 52
0.3%
Primer Barrio 31
0.1%
Regent Quarter 13
0.1%
San Roque 10
< 0.1%
(Missing) 15875
76.5%

Length

2022-07-04T20:05:48.885081 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:49.141102 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
general 3263
32.0%
díaz 3263
32.0%
madame 1255
12.3%
lynch 1255
12.3%
puerta 248
2.4%
de 248
2.4%
la 248
2.4%
alpujarra 248
2.4%
manorá 52
0.5%
primer 31
0.3%
Other values (5) 77
0.8%

Most occurring characters

Value Count Frequency (%)
a 10134
16.7%
e 8357
13.8%
5316
8.8%
n 4593
7.6%
r 4209
6.9%
l 3759
6.2%
G 3263
5.4%
D 3263
5.4%
í 3263
5.4%
z 3263
5.4%
Other values (22) 11239
18.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 45651
75.3%
Uppercase Letter 9692
16.0%
Space Separator 5316
8.8%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 10134
22.2%
e 8357
18.3%
n 4593
10.1%
r 4209
9.2%
l 3759
8.2%
í 3263
7.1%
z 3263
7.1%
d 1503
3.3%
m 1286
2.8%
c 1255
2.7%
Other values (11) 4029
8.8%
Uppercase Letter
Value Count Frequency (%)
G 3263
33.7%
D 3263
33.7%
M 1307
13.5%
L 1255
12.9%
P 279
2.9%
A 248
2.6%
B 31
0.3%
R 23
0.2%
Q 13
0.1%
S 10
0.1%
Space Separator
Value Count Frequency (%)
5316
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 55343
91.2%
Common 5316
8.8%

Most frequent character per script

Latin
Value Count Frequency (%)
a 10134
18.3%
e 8357
15.1%
n 4593
8.3%
r 4209
7.6%
l 3759
6.8%
G 3263
5.9%
D 3263
5.9%
í 3263
5.9%
z 3263
5.9%
d 1503
2.7%
Other values (21) 9736
17.6%
Common
Value Count Frequency (%)
5316
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 57344
94.5%
None 3315
5.5%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 10134
17.7%
e 8357
14.6%
5316
9.3%
n 4593
8.0%
r 4209
7.3%
l 3759
6.6%
G 3263
5.7%
D 3263
5.7%
z 3263
5.7%
d 1503
2.6%
Other values (20) 9684
16.9%
None
Value Count Frequency (%)
í 3263
98.4%
á 52
1.6%

fclass1
Categorical

HIGH CORRELATION
MISSING

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Distinct 14
Distinct (%) 1.1%
Missing 19525
Missing (%) 94.1%
Memory size 162.2 KiB
car_dealership
352
bookshop
206
industrial
200
community_centre
159
restaurant
112
Other values (9)
193

Length

Max length 16
Median length 15
Mean length 11.32242226
Min length 3

Characters and Unicode

Total characters 13836
Distinct characters 22
Distinct categories 2 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row car_dealership
2nd row car_dealership
3rd row car_dealership
4th row car_dealership
5th row car_dealership

Common Values

Value Count Frequency (%)
car_dealership 352
1.7%
bookshop 206
1.0%
industrial 200
1.0%
community_centre 159
0.8%
restaurant 112
0.5%
travel_agent 52
0.3%
cafe 42
0.2%
residential 31
0.1%
atm 24
0.1%
retail 13
0.1%
Other values (4) 31
0.1%
(Missing) 19525
94.1%

Length

2022-07-04T20:05:49.401588 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
car_dealership 352
28.8%
bookshop 206
16.9%
industrial 200
16.4%
community_centre 159
13.0%
restaurant 112
9.2%
travel_agent 52
4.3%
cafe 42
3.4%
residential 31
2.5%
atm 24
2.0%
retail 13
1.1%
Other values (4) 31
2.5%

Most occurring characters

Value Count Frequency (%)
r 1407
10.2%
a 1373
9.9%
e 1366
9.9%
i 1018
7.4%
t 921
6.7%
s 914
6.6%
o 791
5.7%
n 734
5.3%
c 734
5.3%
l 659
4.8%
Other values (12) 3919
28.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 13255
95.8%
Connector Punctuation 581
4.2%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
r 1407
10.6%
a 1373
10.4%
e 1366
10.3%
i 1018
7.7%
t 921
6.9%
s 914
6.9%
o 791
6.0%
n 734
5.5%
c 734
5.5%
l 659
5.0%
Other values (11) 3338
25.2%
Connector Punctuation
Value Count Frequency (%)
_ 581
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 13255
95.8%
Common 581
4.2%

Most frequent character per script

Latin
Value Count Frequency (%)
r 1407
10.6%
a 1373
10.4%
e 1366
10.3%
i 1018
7.7%
t 921
6.9%
s 914
6.9%
o 791
6.0%
n 734
5.5%
c 734
5.5%
l 659
5.0%
Other values (11) 3338
25.2%
Common
Value Count Frequency (%)
_ 581
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 13836
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
r 1407
10.2%
a 1373
9.9%
e 1366
9.9%
i 1018
7.4%
t 921
6.7%
s 914
6.6%
o 791
5.7%
n 734
5.3%
c 734
5.3%
l 659
4.8%
Other values (12) 3919
28.3%

code1
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Distinct 14
Distinct (%) 1.1%
Missing 19525
Missing (%) 94.1%
Infinite 0
Infinite (%) 0.0%
Mean 3427.976268
Minimum 2012
Maximum 7218
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 162.2 KiB
2022-07-04T20:05:49.618808 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 2012
5-th percentile 2012
Q1 2303
median 2541
Q3 2567
95-th percentile 7204
Maximum 7218
Range 5206
Interquartile range (IQR) 264

Descriptive statistics

Standard deviation 1945.236143
Coefficient of variation (CV) 0.567459046
Kurtosis -0.03106928106
Mean 3427.976268
Median Absolute Deviation (MAD) 61
Skewness 1.371167193
Sum 4188987
Variance 3783943.651
Monotonicity Not monotonic
2022-07-04T20:05:49.824132 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
Value Count Frequency (%)
2541 352
1.7%
2515 206
1.0%
7204 200
1.0%
2012 159
0.8%
2301 112
0.5%
2567 52
0.3%
2303 42
0.2%
7203 31
0.1%
2602 24
0.1%
7212 13
0.1%
Other values (4) 31
0.1%
(Missing) 19525
94.1%
Value Count Frequency (%)
2012 159
0.8%
2301 112
0.5%
2302 7
< 0.1%
2303 42
0.2%
2515 206
1.0%
2541 352
1.7%
2567 52
0.3%
2602 24
0.1%
5260 10
< 0.1%
5270 11
0.1%
Value Count Frequency (%)
7218 3
< 0.1%
7212 13
0.1%
7204 200
1.0%
7203 31
0.1%
5270 11
0.1%
5260 10
< 0.1%
2602 24
0.1%
2567 52
0.3%
2541 352
1.7%
2515 206
1.0%

name1
Categorical

HIGH CORRELATION
MISSING

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Distinct 9
Distinct (%) 0.9%
Missing 19773
Missing (%) 95.3%
Memory size 162.2 KiB
Cuevas Hermanos S.A.
352
Rotulación
206
Párbeszéd Háza
159
Omega
112
Viajes Costacruceros
52
Other values (4)
93

Length

Max length 20
Median length 15
Mean length 14.43531828
Min length 5

Characters and Unicode

Total characters 14060
Distinct characters 36
Distinct categories 4 ?
Distinct scripts 2 ?
Distinct blocks 2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Cuevas Hermanos S.A.
2nd row Cuevas Hermanos S.A.
3rd row Cuevas Hermanos S.A.
4th row Cuevas Hermanos S.A.
5th row Cuevas Hermanos S.A.

Common Values

Value Count Frequency (%)
Cuevas Hermanos S.A. 352
1.7%
Rotulación 206
1.0%
Párbeszéd Háza 159
0.8%
Omega 112
0.5%
Viajes Costacruceros 52
0.3%
Gran Capitán 42
0.2%
Segundo Barrio 31
0.1%
Acrevis 13
0.1%
Julian Pizzería 7
< 0.1%
(Missing) 19773
95.3%

Length

2022-07-04T20:05:50.057927 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:50.342229 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
cuevas 352
17.9%
hermanos 352
17.9%
s.a 352
17.9%
rotulación 206
10.5%
párbeszéd 159
8.1%
háza 159
8.1%
omega 112
5.7%
viajes 52
2.6%
costacruceros 52
2.6%
gran 42
2.1%
Other values (6) 131
6.7%

Most occurring characters

Value Count Frequency (%)
a 1414
10.1%
e 1130
8.0%
s 1032
7.3%
995
7.1%
r 739
5.3%
o 724
5.1%
. 704
5.0%
n 680
4.8%
u 648
4.6%
H 511
3.6%
Other values (26) 5483
39.0%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 10040
71.4%
Uppercase Letter 2321
16.5%
Space Separator 995
7.1%
Other Punctuation 704
5.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 1414
14.1%
e 1130
11.3%
s 1032
10.3%
r 739
7.4%
o 724
7.2%
n 680
6.8%
u 648
6.5%
m 464
4.6%
v 365
3.6%
á 360
3.6%
Other values (13) 2484
24.7%
Uppercase Letter
Value Count Frequency (%)
H 511
22.0%
C 446
19.2%
S 383
16.5%
A 365
15.7%
R 206
8.9%
P 166
7.2%
O 112
4.8%
V 52
2.2%
G 42
1.8%
B 31
1.3%
Space Separator
Value Count Frequency (%)
995
100.0%
Other Punctuation
Value Count Frequency (%)
. 704
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 12361
87.9%
Common 1699
12.1%

Most frequent character per script

Latin
Value Count Frequency (%)
a 1414
11.4%
e 1130
9.1%
s 1032
8.3%
r 739
6.0%
o 724
5.9%
n 680
5.5%
u 648
5.2%
H 511
4.1%
m 464
3.8%
C 446
3.6%
Other values (24) 4573
37.0%
Common
Value Count Frequency (%)
995
58.6%
. 704
41.4%

Most occurring blocks

Value Count Frequency (%)
ASCII 13328
94.8%
None 732
5.2%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 1414
10.6%
e 1130
8.5%
s 1032
7.7%
995
7.5%
r 739
5.5%
o 724
5.4%
. 704
5.3%
n 680
5.1%
u 648
4.9%
H 511
3.8%
Other values (22) 4751
35.6%
None
Value Count Frequency (%)
á 360
49.2%
ó 206
28.1%
é 159
21.7%
í 7
1.0%

fclass2
Categorical

HIGH CORRELATION
MISSING

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Distinct 12
Distinct (%) 1.2%
Missing 19740
Missing (%) 95.1%
Memory size 162.2 KiB
industrial
352
pharmacy
206
car_dealership
200
parking
114
butcher
42
Other values (7)
93

Length

Max length 14
Median length 11
Mean length 9.736842105
Min length 4

Characters and Unicode

Total characters 9805
Distinct characters 20
Distinct categories 2 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row industrial
2nd row industrial
3rd row industrial
4th row industrial
5th row industrial

Common Values

Value Count Frequency (%)
industrial 352
1.7%
pharmacy 206
1.0%
car_dealership 200
1.0%
parking 114
0.5%
butcher 42
0.2%
park 25
0.1%
restaurant 18
0.1%
memorial 13
0.1%
supermarket 13
0.1%
hairdresser 11
0.1%
Other values (2) 13
0.1%
(Missing) 19740
95.1%

Length

2022-07-04T20:05:50.644211 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
industrial 352
35.0%
pharmacy 206
20.5%
car_dealership 200
19.9%
parking 114
11.3%
butcher 42
4.2%
park 25
2.5%
restaurant 18
1.8%
memorial 13
1.3%
supermarket 13
1.3%
hairdresser 11
1.1%
Other values (2) 13
1.3%

Most occurring characters

Value Count Frequency (%)
a 1376
14.0%
r 1247
12.7%
i 1052
10.7%
s 615
6.3%
d 573
5.8%
l 568
5.8%
p 561
5.7%
e 540
5.5%
n 497
5.1%
t 466
4.8%
Other values (10) 2310
23.6%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 9605
98.0%
Connector Punctuation 200
2.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 1376
14.3%
r 1247
13.0%
i 1052
11.0%
s 615
6.4%
d 573
6.0%
l 568
5.9%
p 561
5.8%
e 540
5.6%
n 497
5.2%
t 466
4.9%
Other values (9) 2110
22.0%
Connector Punctuation
Value Count Frequency (%)
_ 200
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 9605
98.0%
Common 200
2.0%

Most frequent character per script

Latin
Value Count Frequency (%)
a 1376
14.3%
r 1247
13.0%
i 1052
11.0%
s 615
6.4%
d 573
6.0%
l 568
5.9%
p 561
5.8%
e 540
5.6%
n 497
5.2%
t 466
4.9%
Other values (9) 2110
22.0%
Common
Value Count Frequency (%)
_ 200
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 9805
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 1376
14.0%
r 1247
12.7%
i 1052
10.7%
s 615
6.3%
d 573
5.8%
l 568
5.8%
p 561
5.7%
e 540
5.5%
n 497
5.1%
t 466
4.8%
Other values (10) 2310
23.6%

code2
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Distinct 12
Distinct (%) 1.2%
Missing 19740
Missing (%) 95.1%
Infinite 0
Infinite (%) 0.0%
Mean 4495.449851
Minimum 2006
Maximum 7204
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 162.2 KiB
2022-07-04T20:05:50.852209 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 2006
5-th percentile 2101
Q1 2516
median 2724
Q3 7204
95-th percentile 7204
Maximum 7204
Range 5198
Interquartile range (IQR) 4688

Descriptive statistics

Standard deviation 2281.630205
Coefficient of variation (CV) 0.5075421327
Kurtosis -1.828273171
Mean 4495.449851
Median Absolute Deviation (MAD) 623
Skewness 0.1902441012
Sum 4526918
Variance 5205836.391
Monotonicity Not monotonic
2022-07-04T20:05:51.062902 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
Value Count Frequency (%)
7204 352
1.7%
2101 206
1.0%
2541 200
1.0%
5260 114
0.5%
2516 42
0.2%
7202 25
0.1%
2301 18
0.1%
2724 13
0.1%
2501 13
0.1%
2561 11
0.1%
Other values (2) 13
0.1%
(Missing) 19740
95.1%
Value Count Frequency (%)
2006 3
< 0.1%
2101 206
1.0%
2121 10
< 0.1%
2301 18
0.1%
2501 13
0.1%
2516 42
0.2%
2541 200
1.0%
2561 11
0.1%
2724 13
0.1%
5260 114
0.5%
Value Count Frequency (%)
7204 352
1.7%
7202 25
0.1%
5260 114
0.5%
2724 13
0.1%
2561 11
0.1%
2541 200
1.0%
2516 42
0.2%
2501 13
0.1%
2301 18
0.1%
2121 10
< 0.1%

name2
Categorical

HIGH CORRELATION
MISSING

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Distinct 9
Distinct (%) 1.5%
Missing 20130
Missing (%) 97.0%
Memory size 162.2 KiB
Farmacia Dulce Nombre Islán Molero
206
Cuevas Hermanos S.A.
200
Párbeszéd Háza parkoló
114
Jamones La Encina
42
Vadiandenkmal
13
Other values (4)
42

Length

Max length 34
Median length 25
Mean length 24.30470016
Min length 9

Characters and Unicode

Total characters 14996
Distinct characters 42
Distinct categories 5 ?
Distinct scripts 2 ?
Distinct blocks 2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row El Paraguayo
2nd row El Paraguayo
3rd row El Paraguayo
4th row El Paraguayo
5th row El Paraguayo

Common Values

Value Count Frequency (%)
Farmacia Dulce Nombre Islán Molero 206
1.0%
Cuevas Hermanos S.A. 200
1.0%
Párbeszéd Háza parkoló 114
0.5%
Jamones La Encina 42
0.2%
Vadiandenkmal 13
0.1%
Tesco Metro 13
0.1%
Haarlokal 11
0.1%
Fratelli - Pasta Takeaway 11
0.1%
El Paraguayo 7
< 0.1%
(Missing) 20130
97.0%

Length

2022-07-04T20:05:51.298046 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:51.586453 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
farmacia 206
9.3%
dulce 206
9.3%
nombre 206
9.3%
islán 206
9.3%
molero 206
9.3%
cuevas 200
9.1%
hermanos 200
9.1%
s.a 200
9.1%
párbeszéd 114
5.2%
háza 114
5.2%
Other values (14) 348
15.8%

Most occurring characters

Value Count Frequency (%)
1589
10.6%
a 1531
10.2%
e 1235
8.2%
r 1088
7.3%
o 1018
6.8%
l 796
5.3%
s 786
5.2%
m 667
4.4%
n 558
3.7%
c 467
3.1%
Other values (32) 5261
35.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 10715
71.5%
Uppercase Letter 2281
15.2%
Space Separator 1589
10.6%
Other Punctuation 400
2.7%
Dash Punctuation 11
0.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 1531
14.3%
e 1235
11.5%
r 1088
10.2%
o 1018
9.5%
l 796
7.4%
s 786
7.3%
m 667
6.2%
n 558
5.2%
c 467
4.4%
á 434
4.1%
Other values (14) 2135
19.9%
Uppercase Letter
Value Count Frequency (%)
H 325
14.2%
M 219
9.6%
F 217
9.5%
D 206
9.0%
I 206
9.0%
N 206
9.0%
C 200
8.8%
S 200
8.8%
A 200
8.8%
P 132
5.8%
Other values (5) 170
7.5%
Space Separator
Value Count Frequency (%)
1589
100.0%
Other Punctuation
Value Count Frequency (%)
. 400
100.0%
Dash Punctuation
Value Count Frequency (%)
- 11
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 12996
86.7%
Common 2000
13.3%

Most frequent character per script

Latin
Value Count Frequency (%)
a 1531
11.8%
e 1235
9.5%
r 1088
8.4%
o 1018
7.8%
l 796
6.1%
s 786
6.0%
m 667
5.1%
n 558
4.3%
c 467
3.6%
á 434
3.3%
Other values (29) 4416
34.0%
Common
Value Count Frequency (%)
1589
79.5%
. 400
20.0%
- 11
0.5%

Most occurring blocks

Value Count Frequency (%)
ASCII 14334
95.6%
None 662
4.4%

Most frequent character per block

ASCII
Value Count Frequency (%)
1589
11.1%
a 1531
10.7%
e 1235
8.6%
r 1088
7.6%
o 1018
7.1%
l 796
5.6%
s 786
5.5%
m 667
4.7%
n 558
3.9%
c 467
3.3%
Other values (29) 4599
32.1%
None
Value Count Frequency (%)
á 434
65.6%
é 114
17.2%
ó 114
17.2%

fclass3
Categorical

HIGH CORRELATION
MISSING

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Distinct 10
Distinct (%) 2.4%
Missing 20324
Missing (%) 98.0%
Memory size 162.2 KiB
residential
206
toy_shop
92
supermarket
42
park
25
market_place
13
Other values (5)
45

Length

Max length 15
Median length 11
Mean length 9.36643026
Min length 3

Characters and Unicode

Total characters 3962
Distinct characters 21
Distinct categories 2 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row grass
2nd row grass
3rd row grass
4th row grass
5th row grass

Common Values

Value Count Frequency (%)
residential 206
1.0%
toy_shop 92
0.4%
supermarket 42
0.2%
park 25
0.1%
market_place 13
0.1%
cafe 13
0.1%
atm 11
0.1%
dentist 11
0.1%
grass 7
< 0.1%
parking_bicycle 3
< 0.1%
(Missing) 20324
98.0%

Length

2022-07-04T20:05:51.888954 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:52.171257 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
residential 206
48.7%
toy_shop 92
21.7%
supermarket 42
9.9%
park 25
5.9%
market_place 13
3.1%
cafe 13
3.1%
atm 11
2.6%
dentist 11
2.6%
grass 7
1.7%
parking_bicycle 3
0.7%

Most occurring characters

Value Count Frequency (%)
e 549
13.9%
i 429
10.8%
t 386
9.7%
s 365
9.2%
r 338
8.5%
a 333
8.4%
l 222
5.6%
n 220
5.6%
d 217
5.5%
o 184
4.6%
Other values (11) 719
18.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 3854
97.3%
Connector Punctuation 108
2.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 549
14.2%
i 429
11.1%
t 386
10.0%
s 365
9.5%
r 338
8.8%
a 333
8.6%
l 222
5.8%
n 220
5.7%
d 217
5.6%
o 184
4.8%
Other values (10) 611
15.9%
Connector Punctuation
Value Count Frequency (%)
_ 108
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 3854
97.3%
Common 108
2.7%

Most frequent character per script

Latin
Value Count Frequency (%)
e 549
14.2%
i 429
11.1%
t 386
10.0%
s 365
9.5%
r 338
8.8%
a 333
8.6%
l 222
5.8%
n 220
5.7%
d 217
5.6%
o 184
4.8%
Other values (10) 611
15.9%
Common
Value Count Frequency (%)
_ 108
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 3962
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 549
13.9%
i 429
10.8%
t 386
9.7%
s 365
9.2%
r 338
8.5%
a 333
8.4%
l 222
5.6%
n 220
5.6%
d 217
5.5%
o 184
4.6%
Other values (11) 719
18.1%

code3
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Distinct 10
Distinct (%) 2.4%
Missing 20324
Missing (%) 98.0%
Infinite 0
Infinite (%) 0.0%
Mean 4848.198582
Minimum 2016
Maximum 7218
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 162.2 KiB
2022-07-04T20:05:52.434341 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 2016
5-th percentile 2121
Q1 2526
median 7203
Q3 7203
95-th percentile 7203
Maximum 7218
Range 5202
Interquartile range (IQR) 4677

Descriptive statistics

Standard deviation 2389.870965
Coefficient of variation (CV) 0.492939991
Kurtosis -1.990052706
Mean 4848.198582
Median Absolute Deviation (MAD) 15
Skewness -0.04040123475
Sum 2050788
Variance 5711483.231
Monotonicity Not monotonic
2022-07-04T20:05:52.637640 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
Value Count Frequency (%)
7203 206
1.0%
2526 92
0.4%
2501 42
0.2%
2204 25
0.1%
2016 13
0.1%
2303 13
0.1%
2602 11
0.1%
2121 11
0.1%
7218 7
< 0.1%
5270 3
< 0.1%
(Missing) 20324
98.0%
Value Count Frequency (%)
2016 13
0.1%
2121 11
0.1%
2204 25
0.1%
2303 13
0.1%
2501 42
0.2%
2526 92
0.4%
2602 11
0.1%
5270 3
< 0.1%
7203 206
1.0%
7218 7
< 0.1%
Value Count Frequency (%)
7218 7
< 0.1%
7203 206
1.0%
5270 3
< 0.1%
2602 11
0.1%
2526 92
0.4%
2501 42
0.2%
2303 13
0.1%
2204 25
0.1%
2121 11
0.1%
2016 13
0.1%

name3
Categorical

HIGH CORRELATION
MISSING

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Distinct 6
Distinct (%) 3.3%
Missing 20565
Missing (%) 99.1%
Memory size 162.2 KiB
Metagame
92
Dia
42
St. Galler Bauernmarkt
13
Harris & Hoole
13
Acrevis
11

Length

Max length 28
Median length 8
Mean length 9.423076923
Min length 3

Characters and Unicode

Total characters 1715
Distinct characters 30
Distinct categories 4 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Metagame
2nd row Metagame
3rd row Metagame
4th row Metagame
5th row Metagame

Common Values

Value Count Frequency (%)
Metagame 92
0.4%
Dia 42
0.2%
St. Galler Bauernmarkt 13
0.1%
Harris & Hoole 13
0.1%
Acrevis 11
0.1%
Zahnarztpraxis am Marktplatz 11
0.1%
(Missing) 20565
99.1%

Length

2022-07-04T20:05:52.855005 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:53.104749 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
metagame 92
35.9%
dia 42
16.4%
st 13
5.1%
galler 13
5.1%
bauernmarkt 13
5.1%
harris 13
5.1%
13
5.1%
hoole 13
5.1%
acrevis 11
4.3%
zahnarztpraxis 11
4.3%
Other values (2) 22
8.6%

Most occurring characters

Value Count Frequency (%)
a 344
20.1%
e 234
13.6%
t 151
8.8%
m 116
6.8%
r 109
6.4%
M 103
6.0%
g 92
5.4%
i 77
4.5%
74
4.3%
l 50
2.9%
Other values (20) 365
21.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1383
80.6%
Uppercase Letter 232
13.5%
Space Separator 74
4.3%
Other Punctuation 26
1.5%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 344
24.9%
e 234
16.9%
t 151
10.9%
m 116
8.4%
r 109
7.9%
g 92
6.7%
i 77
5.6%
l 50
3.6%
s 35
2.5%
o 26
1.9%
Other values (9) 149
10.8%
Uppercase Letter
Value Count Frequency (%)
M 103
44.4%
D 42
18.1%
H 26
11.2%
B 13
5.6%
G 13
5.6%
S 13
5.6%
A 11
4.7%
Z 11
4.7%
Other Punctuation
Value Count Frequency (%)
& 13
50.0%
. 13
50.0%
Space Separator
Value Count Frequency (%)
74
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1615
94.2%
Common 100
5.8%

Most frequent character per script

Latin
Value Count Frequency (%)
a 344
21.3%
e 234
14.5%
t 151
9.3%
m 116
7.2%
r 109
6.7%
M 103
6.4%
g 92
5.7%
i 77
4.8%
l 50
3.1%
D 42
2.6%
Other values (17) 297
18.4%
Common
Value Count Frequency (%)
74
74.0%
& 13
13.0%
. 13
13.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1715
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 344
20.1%
e 234
13.6%
t 151
8.8%
m 116
6.8%
r 109
6.4%
M 103
6.0%
g 92
5.4%
i 77
4.5%
74
4.3%
l 50
2.9%
Other values (20) 365
21.3%

fclass4
Categorical

HIGH CORRELATION
MISSING

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Distinct 8
Distinct (%) 1.9%
Missing 20324
Missing (%) 98.0%
Memory size 162.2 KiB
pub
206
restaurant
117
bookshop
42
atm
24
parking_bicycle
13
Other values (3)
21

Length

Max length 15
Median length 3
Mean length 5.957446809
Min length 3

Characters and Unicode

Total characters 2520
Distinct characters 20
Distinct categories 2 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row cafe
2nd row cafe
3rd row cafe
4th row cafe
5th row cafe

Common Values

Value Count Frequency (%)
pub 206
1.0%
restaurant 117
0.6%
bookshop 42
0.2%
atm 24
0.1%
parking_bicycle 13
0.1%
clothes 11
0.1%
cafe 7
< 0.1%
bus_stop 3
< 0.1%
(Missing) 20324
98.0%

Length

2022-07-04T20:05:53.351298 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:53.643755 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
pub 206
48.7%
restaurant 117
27.7%
bookshop 42
9.9%
atm 24
5.7%
parking_bicycle 13
3.1%
clothes 11
2.6%
cafe 7
1.7%
bus_stop 3
0.7%

Most occurring characters

Value Count Frequency (%)
u 326
12.9%
a 278
11.0%
t 272
10.8%
p 264
10.5%
b 264
10.5%
r 247
9.8%
s 176
7.0%
e 148
5.9%
o 140
5.6%
n 130
5.2%
Other values (10) 275
10.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 2504
99.4%
Connector Punctuation 16
0.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
u 326
13.0%
a 278
11.1%
t 272
10.9%
p 264
10.5%
b 264
10.5%
r 247
9.9%
s 176
7.0%
e 148
5.9%
o 140
5.6%
n 130
5.2%
Other values (9) 259
10.3%
Connector Punctuation
Value Count Frequency (%)
_ 16
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 2504
99.4%
Common 16
0.6%

Most frequent character per script

Latin
Value Count Frequency (%)
u 326
13.0%
a 278
11.1%
t 272
10.9%
p 264
10.5%
b 264
10.5%
r 247
9.9%
s 176
7.0%
e 148
5.9%
o 140
5.6%
n 130
5.2%
Other values (9) 259
10.3%
Common
Value Count Frequency (%)
_ 16
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2520
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
u 326
12.9%
a 278
11.0%
t 272
10.8%
p 264
10.5%
b 264
10.5%
r 247
9.8%
s 176
7.0%
e 148
5.9%
o 140
5.6%
n 130
5.2%
Other values (10) 275
10.9%

code4
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Distinct 8
Distinct (%) 1.9%
Missing 20324
Missing (%) 98.0%
Infinite 0
Infinite (%) 0.0%
Mean 2461.099291
Minimum 2301
Maximum 5621
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 162.2 KiB
2022-07-04T20:05:53.912273 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 2301
5-th percentile 2301
Q1 2301
median 2304
Q3 2304
95-th percentile 2602
Maximum 5621
Range 3320
Interquartile range (IQR) 3

Descriptive statistics

Standard deviation 578.8715076
Coefficient of variation (CV) 0.2352085143
Kurtosis 20.70865753
Mean 2461.099291
Median Absolute Deviation (MAD) 1
Skewness 4.683033614
Sum 1041045
Variance 335092.2223
Monotonicity Not monotonic
2022-07-04T20:05:54.096919 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
Value Count Frequency (%)
2304 206
1.0%
2301 117
0.6%
2515 42
0.2%
2602 24
0.1%
5270 13
0.1%
2512 11
0.1%
2303 7
< 0.1%
5621 3
< 0.1%
(Missing) 20324
98.0%
Value Count Frequency (%)
2301 117
0.6%
2303 7
< 0.1%
2304 206
1.0%
2512 11
0.1%
2515 42
0.2%
2602 24
0.1%
5270 13
0.1%
5621 3
< 0.1%
Value Count Frequency (%)
5621 3
< 0.1%
5270 13
0.1%
2602 24
0.1%
2515 42
0.2%
2512 11
0.1%
2304 206
1.0%
2303 7
< 0.1%
2301 117
0.6%

name4
Categorical

HIGH CORRELATION
MISSING

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Distinct 8
Distinct (%) 2.0%
Missing 20350
Missing (%) 98.1%
Memory size 162.2 KiB
Al pan pan y al vino vino
206
Loyola Café
92
Rotulación
42
Restaurante Real
25
Bernies
11
Other values (3)
21

Length

Max length 25
Median length 25
Mean length 18.18891688
Min length 7

Characters and Unicode

Total characters 7221
Distinct characters 26
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Plezier
2nd row Plezier
3rd row Plezier
4th row Plezier
5th row Plezier

Common Values

Value Count Frequency (%)
Al pan pan y al vino vino 206
1.0%
Loyola Café 92
0.4%
Rotulación 42
0.2%
Restaurante Real 25
0.1%
Bernies 11
0.1%
Acrevis 11
0.1%
Plezier 7
< 0.1%
Acton Street 3
< 0.1%
(Missing) 20350
98.1%

Length

2022-07-04T20:05:54.559752 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:54.850420 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
al 412
23.5%
pan 412
23.5%
vino 412
23.5%
y 206
11.8%
loyola 92
5.2%
café 92
5.2%
rotulación 42
2.4%
restaurante 25
1.4%
real 25
1.4%
bernies 11
0.6%
Other values (4) 24
1.4%

Most occurring characters

Value Count Frequency (%)
1356
18.8%
a 919
12.7%
n 905
12.5%
o 641
8.9%
l 578
8.0%
i 483
6.7%
v 423
5.9%
p 412
5.7%
y 298
4.1%
A 220
3.0%
Other values (16) 986
13.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 5348
74.1%
Space Separator 1356
18.8%
Uppercase Letter 517
7.2%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 919
17.2%
n 905
16.9%
o 641
12.0%
l 578
10.8%
i 483
9.0%
v 423
7.9%
p 412
7.7%
y 298
5.6%
e 128
2.4%
t 101
1.9%
Other values (8) 460
8.6%
Uppercase Letter
Value Count Frequency (%)
A 220
42.6%
R 92
17.8%
C 92
17.8%
L 92
17.8%
B 11
2.1%
P 7
1.4%
S 3
0.6%
Space Separator
Value Count Frequency (%)
1356
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 5865
81.2%
Common 1356
18.8%

Most frequent character per script

Latin
Value Count Frequency (%)
a 919
15.7%
n 905
15.4%
o 641
10.9%
l 578
9.9%
i 483
8.2%
v 423
7.2%
p 412
7.0%
y 298
5.1%
A 220
3.8%
e 128
2.2%
Other values (15) 858
14.6%
Common
Value Count Frequency (%)
1356
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 7087
98.1%
None 134
1.9%

Most frequent character per block

ASCII
Value Count Frequency (%)
1356
19.1%
a 919
13.0%
n 905
12.8%
o 641
9.0%
l 578
8.2%
i 483
6.8%
v 423
6.0%
p 412
5.8%
y 298
4.2%
A 220
3.1%
Other values (14) 852
12.0%
None
Value Count Frequency (%)
é 92
68.7%
ó 42
31.3%

fclass5
Categorical

HIGH CORRELATION
MISSING

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Distinct 6
Distinct (%) 1.7%
Missing 20398
Missing (%) 98.3%
Memory size 162.2 KiB
supermarket
206
parking_bicycle
103
jeweller
13
telephone
13
atm
11

Length

Max length 15
Median length 11
Mean length 11.74212034
Min length 3

Characters and Unicode

Total characters 4098
Distinct characters 22
Distinct categories 2 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row parking_bicycle
2nd row parking_bicycle
3rd row parking_bicycle
4th row parking_bicycle
5th row parking_bicycle

Common Values

Value Count Frequency (%)
supermarket 206
1.0%
parking_bicycle 103
0.5%
jeweller 13
0.1%
telephone 13
0.1%
atm 11
0.1%
residential 3
< 0.1%
(Missing) 20398
98.3%

Length

2022-07-04T20:05:55.125981 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:55.382386 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
supermarket 206
59.0%
parking_bicycle 103
29.5%
jeweller 13
3.7%
telephone 13
3.7%
atm 11
3.2%
residential 3
0.9%

Most occurring characters

Value Count Frequency (%)
e 599
14.6%
r 531
13.0%
a 323
7.9%
p 322
7.9%
k 309
7.5%
t 233
5.7%
m 217
5.3%
i 212
5.2%
s 209
5.1%
c 206
5.0%
Other values (12) 937
22.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 3995
97.5%
Connector Punctuation 103
2.5%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 599
15.0%
r 531
13.3%
a 323
8.1%
p 322
8.1%
k 309
7.7%
t 233
5.8%
m 217
5.4%
i 212
5.3%
s 209
5.2%
c 206
5.2%
Other values (11) 834
20.9%
Connector Punctuation
Value Count Frequency (%)
_ 103
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 3995
97.5%
Common 103
2.5%

Most frequent character per script

Latin
Value Count Frequency (%)
e 599
15.0%
r 531
13.3%
a 323
8.1%
p 322
8.1%
k 309
7.7%
t 233
5.8%
m 217
5.4%
i 212
5.3%
s 209
5.2%
c 206
5.2%
Other values (11) 834
20.9%
Common
Value Count Frequency (%)
_ 103
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 4098
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 599
14.6%
r 531
13.0%
a 323
7.9%
p 322
7.9%
k 309
7.5%
t 233
5.7%
m 217
5.3%
i 212
5.2%
s 209
5.1%
c 206
5.0%
Other values (12) 937
22.9%

code5
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Distinct 6
Distinct (%) 1.7%
Missing 20398
Missing (%) 98.3%
Infinite 0
Infinite (%) 0.0%
Mean 3344.083095
Minimum 2006
Maximum 7203
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 162.2 KiB
2022-07-04T20:05:55.587350 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 2006
5-th percentile 2501
Q1 2501
median 2501
Q3 5270
95-th percentile 5270
Maximum 7203
Range 5197
Interquartile range (IQR) 2769

Descriptive statistics

Standard deviation 1325.372889
Coefficient of variation (CV) 0.3963337188
Kurtosis -0.90622483
Mean 3344.083095
Median Absolute Deviation (MAD) 0
Skewness 0.9208670615
Sum 1167085
Variance 1756613.295
Monotonicity Not monotonic
2022-07-04T20:05:55.781568 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
Value Count Frequency (%)
2501 206
1.0%
5270 103
0.5%
2520 13
0.1%
2006 13
0.1%
2602 11
0.1%
7203 3
< 0.1%
(Missing) 20398
98.3%
Value Count Frequency (%)
2006 13
0.1%
2501 206
1.0%
2520 13
0.1%
2602 11
0.1%
5270 103
0.5%
7203 3
< 0.1%
Value Count Frequency (%)
7203 3
< 0.1%
5270 103
0.5%
2602 11
0.1%
2520 13
0.1%
2501 206
1.0%
2006 13
0.1%

name5
Categorical

HIGH CORRELATION
MISSING

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Distinct 2
Distinct (%) 0.9%
Missing 20528
Missing (%) 98.9%
Memory size 162.2 KiB
Dia
206
Goldschmied Gut
13

Length

Max length 15
Median length 3
Mean length 3.712328767
Min length 3

Characters and Unicode

Total characters 813
Distinct characters 15
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Goldschmied Gut
2nd row Goldschmied Gut
3rd row Goldschmied Gut
4th row Goldschmied Gut
5th row Goldschmied Gut

Common Values

Value Count Frequency (%)
Dia 206
1.0%
Goldschmied Gut 13
0.1%
(Missing) 20528
98.9%

Length

2022-07-04T20:05:55.998875 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:56.234391 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
dia 206
88.8%
goldschmied 13
5.6%
gut 13
5.6%

Most occurring characters

Value Count Frequency (%)
i 219
26.9%
D 206
25.3%
a 206
25.3%
G 26
3.2%
d 26
3.2%
o 13
1.6%
l 13
1.6%
s 13
1.6%
c 13
1.6%
h 13
1.6%
Other values (5) 65
8.0%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 568
69.9%
Uppercase Letter 232
28.5%
Space Separator 13
1.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
i 219
38.6%
a 206
36.3%
d 26
4.6%
o 13
2.3%
l 13
2.3%
s 13
2.3%
c 13
2.3%
h 13
2.3%
m 13
2.3%
e 13
2.3%
Other values (2) 26
4.6%
Uppercase Letter
Value Count Frequency (%)
D 206
88.8%
G 26
11.2%
Space Separator
Value Count Frequency (%)
13
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 800
98.4%
Common 13
1.6%

Most frequent character per script

Latin
Value Count Frequency (%)
i 219
27.4%
D 206
25.8%
a 206
25.8%
G 26
3.2%
d 26
3.2%
o 13
1.6%
l 13
1.6%
s 13
1.6%
c 13
1.6%
h 13
1.6%
Other values (4) 52
6.5%
Common
Value Count Frequency (%)
13
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 813
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
i 219
26.9%
D 206
25.3%
a 206
25.3%
G 26
3.2%
d 26
3.2%
o 13
1.6%
l 13
1.6%
s 13
1.6%
c 13
1.6%
h 13
1.6%
Other values (5) 65
8.0%

fclass6
Categorical

HIGH CORRELATION
MISSING

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Distinct 5
Distinct (%) 2.0%
Missing 20493
Missing (%) 98.8%
Memory size 162.2 KiB
cafe
206
atm
13
parking_bicycle
13
memorial
11
restaurant
11

Length

Max length 15
Median length 4
Mean length 4.94488189
Min length 3

Characters and Unicode

Total characters 1256
Distinct characters 19
Distinct categories 2 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row memorial
2nd row memorial
3rd row memorial
4th row memorial
5th row memorial

Common Values

Value Count Frequency (%)
cafe 206
1.0%
atm 13
0.1%
parking_bicycle 13
0.1%
memorial 11
0.1%
restaurant 11
0.1%
(Missing) 20493
98.8%

Length

2022-07-04T20:05:56.436716 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:56.665353 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
cafe 206
81.1%
atm 13
5.1%
parking_bicycle 13
5.1%
memorial 11
4.3%
restaurant 11
4.3%

Most occurring characters

Value Count Frequency (%)
a 265
21.1%
e 241
19.2%
c 232
18.5%
f 206
16.4%
r 46
3.7%
i 37
2.9%
m 35
2.8%
t 35
2.8%
n 24
1.9%
l 24
1.9%
Other values (9) 111
8.8%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1243
99.0%
Connector Punctuation 13
1.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 265
21.3%
e 241
19.4%
c 232
18.7%
f 206
16.6%
r 46
3.7%
i 37
3.0%
m 35
2.8%
t 35
2.8%
n 24
1.9%
l 24
1.9%
Other values (8) 98
7.9%
Connector Punctuation
Value Count Frequency (%)
_ 13
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1243
99.0%
Common 13
1.0%

Most frequent character per script

Latin
Value Count Frequency (%)
a 265
21.3%
e 241
19.4%
c 232
18.7%
f 206
16.6%
r 46
3.7%
i 37
3.0%
m 35
2.8%
t 35
2.8%
n 24
1.9%
l 24
1.9%
Other values (8) 98
7.9%
Common
Value Count Frequency (%)
_ 13
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1256
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 265
21.1%
e 241
19.2%
c 232
18.5%
f 206
16.4%
r 46
3.7%
i 37
2.9%
m 35
2.8%
t 35
2.8%
n 24
1.9%
l 24
1.9%
Other values (9) 111
8.8%

code6
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Distinct 5
Distinct (%) 2.0%
Missing 20493
Missing (%) 98.8%
Memory size 162.2 KiB
2303.0
206
2602.0
13
5270.0
13
2724.0
11
2301.0
11

Length

Max length 6
Median length 6
Mean length 6
Min length 6

Characters and Unicode

Total characters 1524
Distinct characters 9
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 2724.0
2nd row 2724.0
3rd row 2724.0
4th row 2724.0
5th row 2724.0

Common Values

Value Count Frequency (%)
2303.0 206
1.0%
2602.0 13
0.1%
5270.0 13
0.1%
2724.0 11
0.1%
2301.0 11
0.1%
(Missing) 20493
98.8%

Length

2022-07-04T20:05:56.865306 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:57.092437 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
2303.0 206
81.1%
2602.0 13
5.1%
5270.0 13
5.1%
2724.0 11
4.3%
2301.0 11
4.3%

Most occurring characters

Value Count Frequency (%)
0 497
32.6%
3 423
27.8%
2 278
18.2%
. 254
16.7%
7 24
1.6%
6 13
0.9%
5 13
0.9%
4 11
0.7%
1 11
0.7%

Most occurring categories

Value Count Frequency (%)
Decimal Number 1270
83.3%
Other Punctuation 254
16.7%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 497
39.1%
3 423
33.3%
2 278
21.9%
7 24
1.9%
6 13
1.0%
5 13
1.0%
4 11
0.9%
1 11
0.9%
Other Punctuation
Value Count Frequency (%)
. 254
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 1524
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 497
32.6%
3 423
27.8%
2 278
18.2%
. 254
16.7%
7 24
1.6%
6 13
0.9%
5 13
0.9%
4 11
0.7%
1 11
0.7%

Most occurring blocks

Value Count Frequency (%)
ASCII 1524
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 497
32.6%
3 423
27.8%
2 278
18.2%
. 254
16.7%
7 24
1.6%
6 13
0.9%
5 13
0.9%
4 11
0.7%
1 11
0.7%

name6
Categorical

HIGH CORRELATION
MISSING

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Distinct 3
Distinct (%) 1.3%
Missing 20519
Missing (%) 98.9%
Memory size 162.2 KiB
Gran Capitán
206
Vadiandenkmal
11
Marktplatz
11

Length

Max length 13
Median length 12
Mean length 11.95175439
Min length 10

Characters and Unicode

Total characters 2725
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Vadiandenkmal
2nd row Vadiandenkmal
3rd row Vadiandenkmal
4th row Vadiandenkmal
5th row Vadiandenkmal

Common Values

Value Count Frequency (%)
Gran Capitán 206
1.0%
Vadiandenkmal 11
0.1%
Marktplatz 11
0.1%
(Missing) 20519
98.9%

Length

2022-07-04T20:05:57.302656 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:57.530038 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
gran 206
47.5%
capitán 206
47.5%
vadiandenkmal 11
2.5%
marktplatz 11
2.5%

Most occurring characters

Value Count Frequency (%)
a 467
17.1%
n 434
15.9%
t 228
8.4%
i 217
8.0%
r 217
8.0%
p 217
8.0%
G 206
7.6%
á 206
7.6%
C 206
7.6%
206
7.6%
Other values (8) 121
4.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 2085
76.5%
Uppercase Letter 434
15.9%
Space Separator 206
7.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 467
22.4%
n 434
20.8%
t 228
10.9%
i 217
10.4%
r 217
10.4%
p 217
10.4%
á 206
9.9%
d 22
1.1%
k 22
1.1%
l 22
1.1%
Other values (3) 33
1.6%
Uppercase Letter
Value Count Frequency (%)
G 206
47.5%
C 206
47.5%
V 11
2.5%
M 11
2.5%
Space Separator
Value Count Frequency (%)
206
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 2519
92.4%
Common 206
7.6%

Most frequent character per script

Latin
Value Count Frequency (%)
a 467
18.5%
n 434
17.2%
t 228
9.1%
i 217
8.6%
r 217
8.6%
p 217
8.6%
G 206
8.2%
á 206
8.2%
C 206
8.2%
d 22
0.9%
Other values (7) 99
3.9%
Common
Value Count Frequency (%)
206
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2519
92.4%
None 206
7.6%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 467
18.5%
n 434
17.2%
t 228
9.1%
i 217
8.6%
r 217
8.6%
p 217
8.6%
G 206
8.2%
C 206
8.2%
206
8.2%
d 22
0.9%
Other values (7) 99
3.9%
None
Value Count Frequency (%)
á 206
100.0%

fclass7
Categorical

HIGH CORRELATION
MISSING

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Distinct 3
Distinct (%) 1.3%
Missing 20517
Missing (%) 98.9%
Memory size 162.2 KiB
butcher
206
residential
13
dentist
11

Length

Max length 11
Median length 7
Mean length 7.226086957
Min length 7

Characters and Unicode

Total characters 1662
Distinct characters 13
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row dentist
2nd row dentist
3rd row dentist
4th row dentist
5th row dentist

Common Values

Value Count Frequency (%)
butcher 206
1.0%
residential 13
0.1%
dentist 11
0.1%
(Missing) 20517
98.9%

Length

2022-07-04T20:05:57.730245 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:57.959574 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
butcher 206
89.6%
residential 13
5.7%
dentist 11
4.8%

Most occurring characters

Value Count Frequency (%)
e 243
14.6%
t 241
14.5%
r 219
13.2%
b 206
12.4%
u 206
12.4%
c 206
12.4%
h 206
12.4%
i 37
2.2%
s 24
1.4%
d 24
1.4%
Other values (3) 50
3.0%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1662
100.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 243
14.6%
t 241
14.5%
r 219
13.2%
b 206
12.4%
u 206
12.4%
c 206
12.4%
h 206
12.4%
i 37
2.2%
s 24
1.4%
d 24
1.4%
Other values (3) 50
3.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1662
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 243
14.6%
t 241
14.5%
r 219
13.2%
b 206
12.4%
u 206
12.4%
c 206
12.4%
h 206
12.4%
i 37
2.2%
s 24
1.4%
d 24
1.4%
Other values (3) 50
3.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1662
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 243
14.6%
t 241
14.5%
r 219
13.2%
b 206
12.4%
u 206
12.4%
c 206
12.4%
h 206
12.4%
i 37
2.2%
s 24
1.4%
d 24
1.4%
Other values (3) 50
3.0%

code7
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Distinct 3
Distinct (%) 1.3%
Missing 20517
Missing (%) 98.9%
Memory size 162.2 KiB
2516.0
206
7203.0
13
2121.0
11

Length

Max length 6
Median length 6
Mean length 6
Min length 6

Characters and Unicode

Total characters 1380
Distinct characters 8
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 2121.0
2nd row 2121.0
3rd row 2121.0
4th row 2121.0
5th row 2121.0

Common Values

Value Count Frequency (%)
2516.0 206
1.0%
7203.0 13
0.1%
2121.0 11
0.1%
(Missing) 20517
98.9%

Length

2022-07-04T20:05:58.142360 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:58.348963 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
2516.0 206
89.6%
7203.0 13
5.7%
2121.0 11
4.8%

Most occurring characters

Value Count Frequency (%)
0 243
17.6%
2 241
17.5%
. 230
16.7%
1 228
16.5%
5 206
14.9%
6 206
14.9%
7 13
0.9%
3 13
0.9%

Most occurring categories

Value Count Frequency (%)
Decimal Number 1150
83.3%
Other Punctuation 230
16.7%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 243
21.1%
2 241
21.0%
1 228
19.8%
5 206
17.9%
6 206
17.9%
7 13
1.1%
3 13
1.1%
Other Punctuation
Value Count Frequency (%)
. 230
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 1380
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 243
17.6%
2 241
17.5%
. 230
16.7%
1 228
16.5%
5 206
14.9%
6 206
14.9%
7 13
0.9%
3 13
0.9%

Most occurring blocks

Value Count Frequency (%)
ASCII 1380
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 243
17.6%
2 241
17.5%
. 230
16.7%
1 228
16.5%
5 206
14.9%
6 206
14.9%
7 13
0.9%
3 13
0.9%

name7
Categorical

HIGH CORRELATION
MISSING

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Distinct 2
Distinct (%) 0.9%
Missing 20530
Missing (%) 99.0%
Memory size 162.2 KiB
Jamones La Encina
206
Zahnarztpraxis am Marktplatz
11

Length

Max length 28
Median length 17
Mean length 17.55760369
Min length 17

Characters and Unicode

Total characters 3810
Distinct characters 22
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Zahnarztpraxis am Marktplatz
2nd row Zahnarztpraxis am Marktplatz
3rd row Zahnarztpraxis am Marktplatz
4th row Zahnarztpraxis am Marktplatz
5th row Zahnarztpraxis am Marktplatz

Common Values

Value Count Frequency (%)
Jamones La Encina 206
1.0%
Zahnarztpraxis am Marktplatz 11
0.1%
(Missing) 20530
99.0%

Length

2022-07-04T20:05:58.539801 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:58.773969 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
jamones 206
31.6%
la 206
31.6%
encina 206
31.6%
zahnarztpraxis 11
1.7%
am 11
1.7%
marktplatz 11
1.7%

Most occurring characters

Value Count Frequency (%)
a 684
18.0%
n 629
16.5%
434
11.4%
i 217
5.7%
m 217
5.7%
s 217
5.7%
E 206
5.4%
c 206
5.4%
J 206
5.4%
L 206
5.4%
Other values (12) 588
15.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 2736
71.8%
Uppercase Letter 640
16.8%
Space Separator 434
11.4%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 684
25.0%
n 629
23.0%
i 217
7.9%
m 217
7.9%
s 217
7.9%
c 206
7.5%
e 206
7.5%
o 206
7.5%
r 33
1.2%
t 33
1.2%
Other values (6) 88
3.2%
Uppercase Letter
Value Count Frequency (%)
E 206
32.2%
J 206
32.2%
L 206
32.2%
Z 11
1.7%
M 11
1.7%
Space Separator
Value Count Frequency (%)
434
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 3376
88.6%
Common 434
11.4%

Most frequent character per script

Latin
Value Count Frequency (%)
a 684
20.3%
n 629
18.6%
i 217
6.4%
m 217
6.4%
s 217
6.4%
E 206
6.1%
c 206
6.1%
J 206
6.1%
L 206
6.1%
e 206
6.1%
Other values (11) 382
11.3%
Common
Value Count Frequency (%)
434
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 3810
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 684
18.0%
n 629
16.5%
434
11.4%
i 217
5.7%
m 217
5.7%
s 217
5.7%
E 206
5.4%
c 206
5.4%
J 206
5.4%
L 206
5.4%
Other values (12) 588
15.4%

fclass8
Categorical

HIGH CORRELATION
MISSING

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Distinct 3
Distinct (%) 1.3%
Missing 20517
Missing (%) 98.9%
Memory size 162.2 KiB
hairdresser
206
hotel
13
fountain
11

Length

Max length 11
Median length 11
Mean length 10.5173913
Min length 5

Characters and Unicode

Total characters 2419
Distinct characters 13
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row fountain
2nd row fountain
3rd row fountain
4th row fountain
5th row fountain

Common Values

Value Count Frequency (%)
hairdresser 206
1.0%
hotel 13
0.1%
fountain 11
0.1%
(Missing) 20517
98.9%

Length

2022-07-04T20:05:58.983145 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:59.208572 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
hairdresser 206
89.6%
hotel 13
5.7%
fountain 11
4.8%

Most occurring characters

Value Count Frequency (%)
r 618
25.5%
e 425
17.6%
s 412
17.0%
h 219
9.1%
a 217
9.0%
i 217
9.0%
d 206
8.5%
o 24
1.0%
t 24
1.0%
n 22
0.9%
Other values (3) 35
1.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 2419
100.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
r 618
25.5%
e 425
17.6%
s 412
17.0%
h 219
9.1%
a 217
9.0%
i 217
9.0%
d 206
8.5%
o 24
1.0%
t 24
1.0%
n 22
0.9%
Other values (3) 35
1.4%

Most occurring scripts

Value Count Frequency (%)
Latin 2419
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
r 618
25.5%
e 425
17.6%
s 412
17.0%
h 219
9.1%
a 217
9.0%
i 217
9.0%
d 206
8.5%
o 24
1.0%
t 24
1.0%
n 22
0.9%
Other values (3) 35
1.4%

Most occurring blocks

Value Count Frequency (%)
ASCII 2419
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
r 618
25.5%
e 425
17.6%
s 412
17.0%
h 219
9.1%
a 217
9.0%
i 217
9.0%
d 206
8.5%
o 24
1.0%
t 24
1.0%
n 22
0.9%
Other values (3) 35
1.4%

code8
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Distinct 3
Distinct (%) 1.3%
Missing 20517
Missing (%) 98.9%
Memory size 162.2 KiB
2561.0
206
2401.0
13
2904.0
11

Length

Max length 6
Median length 6
Mean length 6
Min length 6

Characters and Unicode

Total characters 1380
Distinct characters 8
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 2904.0
2nd row 2904.0
3rd row 2904.0
4th row 2904.0
5th row 2904.0

Common Values

Value Count Frequency (%)
2561.0 206
1.0%
2401.0 13
0.1%
2904.0 11
0.1%
(Missing) 20517
98.9%

Length

2022-07-04T20:05:59.391462 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:05:59.593694 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
2561.0 206
89.6%
2401.0 13
5.7%
2904.0 11
4.8%

Most occurring characters

Value Count Frequency (%)
0 254
18.4%
2 230
16.7%
. 230
16.7%
1 219
15.9%
5 206
14.9%
6 206
14.9%
4 24
1.7%
9 11
0.8%

Most occurring categories

Value Count Frequency (%)
Decimal Number 1150
83.3%
Other Punctuation 230
16.7%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 254
22.1%
2 230
20.0%
1 219
19.0%
5 206
17.9%
6 206
17.9%
4 24
2.1%
9 11
1.0%
Other Punctuation
Value Count Frequency (%)
. 230
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 1380
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 254
18.4%
2 230
16.7%
. 230
16.7%
1 219
15.9%
5 206
14.9%
6 206
14.9%
4 24
1.7%
9 11
0.8%

Most occurring blocks

Value Count Frequency (%)
ASCII 1380
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 254
18.4%
2 230
16.7%
. 230
16.7%
1 219
15.9%
5 206
14.9%
6 206
14.9%
4 24
1.7%
9 11
0.8%

name8
Categorical

HIGH CORRELATION
MISSING

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Distinct 2
Distinct (%) 0.9%
Missing 20528
Missing (%) 98.9%
Memory size 162.2 KiB
Razza
206
Premier Inn
13

Length

Max length 11
Median length 5
Mean length 5.356164384
Min length 5

Characters and Unicode

Total characters 1173
Distinct characters 11
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Premier Inn
2nd row Premier Inn
3rd row Premier Inn
4th row Premier Inn
5th row Premier Inn

Common Values

Value Count Frequency (%)
Razza 206
1.0%
Premier Inn 13
0.1%
(Missing) 20528
98.9%

Length

2022-07-04T20:05:59.787461 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:00.024327 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
razza 206
88.8%
premier 13
5.6%
inn 13
5.6%

Most occurring characters

Value Count Frequency (%)
a 412
35.1%
z 412
35.1%
R 206
17.6%
r 26
2.2%
e 26
2.2%
n 26
2.2%
P 13
1.1%
m 13
1.1%
i 13
1.1%
13
1.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 928
79.1%
Uppercase Letter 232
19.8%
Space Separator 13
1.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 412
44.4%
z 412
44.4%
r 26
2.8%
e 26
2.8%
n 26
2.8%
m 13
1.4%
i 13
1.4%
Uppercase Letter
Value Count Frequency (%)
R 206
88.8%
P 13
5.6%
I 13
5.6%
Space Separator
Value Count Frequency (%)
13
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1160
98.9%
Common 13
1.1%

Most frequent character per script

Latin
Value Count Frequency (%)
a 412
35.5%
z 412
35.5%
R 206
17.8%
r 26
2.2%
e 26
2.2%
n 26
2.2%
P 13
1.1%
m 13
1.1%
i 13
1.1%
I 13
1.1%
Common
Value Count Frequency (%)
13
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1173
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 412
35.1%
z 412
35.1%
R 206
17.6%
r 26
2.2%
e 26
2.2%
n 26
2.2%
P 13
1.1%
m 13
1.1%
i 13
1.1%
13
1.1%

fclass9
Categorical

HIGH CORRELATION
MISSING

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Distinct 2
Distinct (%) 0.9%
Missing 20517
Missing (%) 98.9%
Memory size 162.2 KiB
restaurant
217
bar
13

Length

Max length 10
Median length 10
Mean length 9.604347826
Min length 3

Characters and Unicode

Total characters 2209
Distinct characters 8
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row restaurant
2nd row restaurant
3rd row restaurant
4th row restaurant
5th row restaurant

Common Values

Value Count Frequency (%)
restaurant 217
1.0%
bar 13
0.1%
(Missing) 20517
98.9%

Length

2022-07-04T20:06:00.221288 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:00.433324 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
restaurant 217
94.3%
bar 13
5.7%

Most occurring characters

Value Count Frequency (%)
r 447
20.2%
a 447
20.2%
t 434
19.6%
e 217
9.8%
s 217
9.8%
u 217
9.8%
n 217
9.8%
b 13
0.6%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 2209
100.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
r 447
20.2%
a 447
20.2%
t 434
19.6%
e 217
9.8%
s 217
9.8%
u 217
9.8%
n 217
9.8%
b 13
0.6%

Most occurring scripts

Value Count Frequency (%)
Latin 2209
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
r 447
20.2%
a 447
20.2%
t 434
19.6%
e 217
9.8%
s 217
9.8%
u 217
9.8%
n 217
9.8%
b 13
0.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 2209
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
r 447
20.2%
a 447
20.2%
t 434
19.6%
e 217
9.8%
s 217
9.8%
u 217
9.8%
n 217
9.8%
b 13
0.6%

code9
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Distinct 2
Distinct (%) 0.9%
Missing 20517
Missing (%) 98.9%
Memory size 162.2 KiB
2301.0
217
2305.0
13

Length

Max length 6
Median length 6
Mean length 6
Min length 6

Characters and Unicode

Total characters 1380
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 2301.0
2nd row 2301.0
3rd row 2301.0
4th row 2301.0
5th row 2301.0

Common Values

Value Count Frequency (%)
2301.0 217
1.0%
2305.0 13
0.1%
(Missing) 20517
98.9%

Length

2022-07-04T20:06:00.603862 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:01.032133 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
2301.0 217
94.3%
2305.0 13
5.7%

Most occurring characters

Value Count Frequency (%)
0 460
33.3%
2 230
16.7%
3 230
16.7%
. 230
16.7%
1 217
15.7%
5 13
0.9%

Most occurring categories

Value Count Frequency (%)
Decimal Number 1150
83.3%
Other Punctuation 230
16.7%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 460
40.0%
2 230
20.0%
3 230
20.0%
1 217
18.9%
5 13
1.1%
Other Punctuation
Value Count Frequency (%)
. 230
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 1380
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 460
33.3%
2 230
16.7%
3 230
16.7%
. 230
16.7%
1 217
15.7%
5 13
0.9%

Most occurring blocks

Value Count Frequency (%)
ASCII 1380
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 460
33.3%
2 230
16.7%
3 230
16.7%
. 230
16.7%
1 217
15.7%
5 13
0.9%

name9
Categorical

HIGH CORRELATION
MISSING

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Distinct 3
Distinct (%) 1.3%
Missing 20517
Missing (%) 98.9%
Memory size 162.2 KiB
La Pataleta
206
Be At One
13
Fratelli - Pasta Takeaway
11

Length

Max length 25
Median length 11
Mean length 11.55652174
Min length 9

Characters and Unicode

Total characters 2658
Distinct characters 20
Distinct categories 4 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Fratelli - Pasta Takeaway
2nd row Fratelli - Pasta Takeaway
3rd row Fratelli - Pasta Takeaway
4th row Fratelli - Pasta Takeaway
5th row Fratelli - Pasta Takeaway

Common Values

Value Count Frequency (%)
La Pataleta 206
1.0%
Be At One 13
0.1%
Fratelli - Pasta Takeaway 11
0.1%
(Missing) 20517
98.9%

Length

2022-07-04T20:06:01.212325 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:01.437977 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
la 206
41.6%
pataleta 206
41.6%
be 13
2.6%
at 13
2.6%
one 13
2.6%
fratelli 11
2.2%
11
2.2%
pasta 11
2.2%
takeaway 11
2.2%

Most occurring characters

Value Count Frequency (%)
a 890
33.5%
t 447
16.8%
265
10.0%
e 254
9.6%
l 228
8.6%
P 217
8.2%
L 206
7.8%
O 13
0.5%
n 13
0.5%
A 13
0.5%
Other values (10) 112
4.2%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1898
71.4%
Uppercase Letter 484
18.2%
Space Separator 265
10.0%
Dash Punctuation 11
0.4%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 890
46.9%
t 447
23.6%
e 254
13.4%
l 228
12.0%
n 13
0.7%
r 11
0.6%
i 11
0.6%
s 11
0.6%
k 11
0.6%
w 11
0.6%
Uppercase Letter
Value Count Frequency (%)
P 217
44.8%
L 206
42.6%
O 13
2.7%
A 13
2.7%
B 13
2.7%
F 11
2.3%
T 11
2.3%
Space Separator
Value Count Frequency (%)
265
100.0%
Dash Punctuation
Value Count Frequency (%)
- 11
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 2382
89.6%
Common 276
10.4%

Most frequent character per script

Latin
Value Count Frequency (%)
a 890
37.4%
t 447
18.8%
e 254
10.7%
l 228
9.6%
P 217
9.1%
L 206
8.6%
O 13
0.5%
n 13
0.5%
A 13
0.5%
B 13
0.5%
Other values (8) 88
3.7%
Common
Value Count Frequency (%)
265
96.0%
- 11
4.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2658
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 890
33.5%
t 447
16.8%
265
10.0%
e 254
9.6%
l 228
8.6%
P 217
8.2%
L 206
7.8%
O 13
0.5%
n 13
0.5%
A 13
0.5%
Other values (10) 112
4.2%

fclass10
Categorical

HIGH CORRELATION
MISSING

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Distinct 3
Distinct (%) 1.3%
Missing 20517
Missing (%) 98.9%
Memory size 162.2 KiB
beauty_shop
206
pub
13
market_place
11

Length

Max length 12
Median length 11
Mean length 10.59565217
Min length 3

Characters and Unicode

Total characters 2437
Distinct characters 16
Distinct categories 2 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row market_place
2nd row market_place
3rd row market_place
4th row market_place
5th row market_place

Common Values

Value Count Frequency (%)
beauty_shop 206
1.0%
pub 13
0.1%
market_place 11
0.1%
(Missing) 20517
98.9%

Length

2022-07-04T20:06:01.626353 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:01.832461 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
beauty_shop 206
89.6%
pub 13
5.7%
market_place 11
4.8%

Most occurring characters

Value Count Frequency (%)
p 230
9.4%
e 228
9.4%
a 228
9.4%
b 219
9.0%
u 219
9.0%
t 217
8.9%
_ 217
8.9%
y 206
8.5%
s 206
8.5%
h 206
8.5%
Other values (6) 261
10.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 2220
91.1%
Connector Punctuation 217
8.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
p 230
10.4%
e 228
10.3%
a 228
10.3%
b 219
9.9%
u 219
9.9%
t 217
9.8%
y 206
9.3%
s 206
9.3%
h 206
9.3%
o 206
9.3%
Other values (5) 55
2.5%
Connector Punctuation
Value Count Frequency (%)
_ 217
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 2220
91.1%
Common 217
8.9%

Most frequent character per script

Latin
Value Count Frequency (%)
p 230
10.4%
e 228
10.3%
a 228
10.3%
b 219
9.9%
u 219
9.9%
t 217
9.8%
y 206
9.3%
s 206
9.3%
h 206
9.3%
o 206
9.3%
Other values (5) 55
2.5%
Common
Value Count Frequency (%)
_ 217
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2437
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
p 230
9.4%
e 228
9.4%
a 228
9.4%
b 219
9.0%
u 219
9.0%
t 217
8.9%
_ 217
8.9%
y 206
8.5%
s 206
8.5%
h 206
8.5%
Other values (6) 261
10.7%

code10
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Distinct 3
Distinct (%) 1.3%
Missing 20517
Missing (%) 98.9%
Memory size 162.2 KiB
2529.0
206
2304.0
13
2016.0
11

Length

Max length 6
Median length 6
Mean length 6
Min length 6

Characters and Unicode

Total characters 1380
Distinct characters 9
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 2016.0
2nd row 2016.0
3rd row 2016.0
4th row 2016.0
5th row 2016.0

Common Values

Value Count Frequency (%)
2529.0 206
1.0%
2304.0 13
0.1%
2016.0 11
0.1%
(Missing) 20517
98.9%

Length

2022-07-04T20:06:02.016328 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:02.229705 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
2529.0 206
89.6%
2304.0 13
5.7%
2016.0 11
4.8%

Most occurring characters

Value Count Frequency (%)
2 436
31.6%
0 254
18.4%
. 230
16.7%
5 206
14.9%
9 206
14.9%
3 13
0.9%
4 13
0.9%
1 11
0.8%
6 11
0.8%

Most occurring categories

Value Count Frequency (%)
Decimal Number 1150
83.3%
Other Punctuation 230
16.7%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
2 436
37.9%
0 254
22.1%
5 206
17.9%
9 206
17.9%
3 13
1.1%
4 13
1.1%
1 11
1.0%
6 11
1.0%
Other Punctuation
Value Count Frequency (%)
. 230
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 1380
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
2 436
31.6%
0 254
18.4%
. 230
16.7%
5 206
14.9%
9 206
14.9%
3 13
0.9%
4 13
0.9%
1 11
0.8%
6 11
0.8%

Most occurring blocks

Value Count Frequency (%)
ASCII 1380
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
2 436
31.6%
0 254
18.4%
. 230
16.7%
5 206
14.9%
9 206
14.9%
3 13
0.9%
4 13
0.9%
1 11
0.8%
6 11
0.8%

name10
Categorical

HIGH CORRELATION
MISSING

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Distinct 3
Distinct (%) 1.3%
Missing 20517
Missing (%) 98.9%
Memory size 162.2 KiB
Jerónimo
206
Millers
13
St. Galler Bauernmarkt
11

Length

Max length 22
Median length 8
Mean length 8.613043478
Min length 7

Characters and Unicode

Total characters 1981
Distinct characters 20
Distinct categories 4 ?
Distinct scripts 2 ?
Distinct blocks 2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row St. Galler Bauernmarkt
2nd row St. Galler Bauernmarkt
3rd row St. Galler Bauernmarkt
4th row St. Galler Bauernmarkt
5th row St. Galler Bauernmarkt

Common Values

Value Count Frequency (%)
Jerónimo 206
1.0%
Millers 13
0.1%
St. Galler Bauernmarkt 11
0.1%
(Missing) 20517
98.9%

Length

2022-07-04T20:06:02.423091 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:02.649899 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
jerónimo 206
81.7%
millers 13
5.2%
st 11
4.4%
galler 11
4.4%
bauernmarkt 11
4.4%

Most occurring characters

Value Count Frequency (%)
r 252
12.7%
e 241
12.2%
i 219
11.1%
n 217
11.0%
m 217
11.0%
J 206
10.4%
ó 206
10.4%
o 206
10.4%
l 48
2.4%
a 33
1.7%
Other values (10) 136
6.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1696
85.6%
Uppercase Letter 252
12.7%
Space Separator 22
1.1%
Other Punctuation 11
0.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
r 252
14.9%
e 241
14.2%
i 219
12.9%
n 217
12.8%
m 217
12.8%
ó 206
12.1%
o 206
12.1%
l 48
2.8%
a 33
1.9%
t 22
1.3%
Other values (3) 35
2.1%
Uppercase Letter
Value Count Frequency (%)
J 206
81.7%
M 13
5.2%
S 11
4.4%
G 11
4.4%
B 11
4.4%
Space Separator
Value Count Frequency (%)
22
100.0%
Other Punctuation
Value Count Frequency (%)
. 11
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1948
98.3%
Common 33
1.7%

Most frequent character per script

Latin
Value Count Frequency (%)
r 252
12.9%
e 241
12.4%
i 219
11.2%
n 217
11.1%
m 217
11.1%
J 206
10.6%
ó 206
10.6%
o 206
10.6%
l 48
2.5%
a 33
1.7%
Other values (8) 103
5.3%
Common
Value Count Frequency (%)
22
66.7%
. 11
33.3%

Most occurring blocks

Value Count Frequency (%)
ASCII 1775
89.6%
None 206
10.4%

Most frequent character per block

ASCII
Value Count Frequency (%)
r 252
14.2%
e 241
13.6%
i 219
12.3%
n 217
12.2%
m 217
12.2%
J 206
11.6%
o 206
11.6%
l 48
2.7%
a 33
1.9%
t 22
1.2%
Other values (9) 114
6.4%
None
Value Count Frequency (%)
ó 206
100.0%

fclass11
Categorical

HIGH CORRELATION
MISSING

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Distinct 2
Distinct (%) 8.3%
Missing 20723
Missing (%) 99.9%
Memory size 162.2 KiB
restaurant
13
laundry
11

Length

Max length 10
Median length 10
Mean length 8.625
Min length 7

Characters and Unicode

Total characters 207
Distinct characters 10
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row laundry
2nd row laundry
3rd row laundry
4th row laundry
5th row laundry

Common Values

Value Count Frequency (%)
restaurant 13
0.1%
laundry 11
0.1%
(Missing) 20723
99.9%

Length

2022-07-04T20:06:02.850491 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:03.069498 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
restaurant 13
54.2%
laundry 11
45.8%

Most occurring characters

Value Count Frequency (%)
r 37
17.9%
a 37
17.9%
t 26
12.6%
u 24
11.6%
n 24
11.6%
e 13
6.3%
s 13
6.3%
l 11
5.3%
d 11
5.3%
y 11
5.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 207
100.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
r 37
17.9%
a 37
17.9%
t 26
12.6%
u 24
11.6%
n 24
11.6%
e 13
6.3%
s 13
6.3%
l 11
5.3%
d 11
5.3%
y 11
5.3%

Most occurring scripts

Value Count Frequency (%)
Latin 207
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
r 37
17.9%
a 37
17.9%
t 26
12.6%
u 24
11.6%
n 24
11.6%
e 13
6.3%
s 13
6.3%
l 11
5.3%
d 11
5.3%
y 11
5.3%

Most occurring blocks

Value Count Frequency (%)
ASCII 207
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
r 37
17.9%
a 37
17.9%
t 26
12.6%
u 24
11.6%
n 24
11.6%
e 13
6.3%
s 13
6.3%
l 11
5.3%
d 11
5.3%
y 11
5.3%

code11
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Distinct 2
Distinct (%) 8.3%
Missing 20723
Missing (%) 99.9%
Memory size 162.2 KiB
2301.0
13
2568.0
11

Length

Max length 6
Median length 6
Mean length 6
Min length 6

Characters and Unicode

Total characters 144
Distinct characters 8
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 2568.0
2nd row 2568.0
3rd row 2568.0
4th row 2568.0
5th row 2568.0

Common Values

Value Count Frequency (%)
2301.0 13
0.1%
2568.0 11
0.1%
(Missing) 20723
99.9%

Length

2022-07-04T20:06:03.250727 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:03.455230 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
2301.0 13
54.2%
2568.0 11
45.8%

Most occurring characters

Value Count Frequency (%)
0 37
25.7%
2 24
16.7%
. 24
16.7%
3 13
9.0%
1 13
9.0%
5 11
7.6%
6 11
7.6%
8 11
7.6%

Most occurring categories

Value Count Frequency (%)
Decimal Number 120
83.3%
Other Punctuation 24
16.7%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 37
30.8%
2 24
20.0%
3 13
10.8%
1 13
10.8%
5 11
9.2%
6 11
9.2%
8 11
9.2%
Other Punctuation
Value Count Frequency (%)
. 24
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 144
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 37
25.7%
2 24
16.7%
. 24
16.7%
3 13
9.0%
1 13
9.0%
5 11
7.6%
6 11
7.6%
8 11
7.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 144
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 37
25.7%
2 24
16.7%
. 24
16.7%
3 13
9.0%
1 13
9.0%
5 11
7.6%
6 11
7.6%
8 11
7.6%

name11
Categorical

HIGH CORRELATION
MISSING

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Distinct 2
Distinct (%) 8.3%
Missing 20723
Missing (%) 99.9%
Memory size 162.2 KiB
Kitchin
13
Bernet Textilpflege
11

Length

Max length 19
Median length 7
Mean length 12.5
Min length 7

Characters and Unicode

Total characters 300
Distinct characters 16
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Bernet Textilpflege
2nd row Bernet Textilpflege
3rd row Bernet Textilpflege
4th row Bernet Textilpflege
5th row Bernet Textilpflege

Common Values

Value Count Frequency (%)
Kitchin 13
0.1%
Bernet Textilpflege 11
0.1%
(Missing) 20723
99.9%

Length

2022-07-04T20:06:03.643509 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:03.862371 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
kitchin 13
37.1%
bernet 11
31.4%
textilpflege 11
31.4%

Most occurring characters

Value Count Frequency (%)
e 55
18.3%
i 37
12.3%
t 35
11.7%
n 24
8.0%
l 22
7.3%
K 13
4.3%
c 13
4.3%
h 13
4.3%
B 11
3.7%
r 11
3.7%
Other values (6) 66
22.0%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 254
84.7%
Uppercase Letter 35
11.7%
Space Separator 11
3.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 55
21.7%
i 37
14.6%
t 35
13.8%
n 24
9.4%
l 22
8.7%
c 13
5.1%
h 13
5.1%
r 11
4.3%
x 11
4.3%
p 11
4.3%
Other values (2) 22
8.7%
Uppercase Letter
Value Count Frequency (%)
K 13
37.1%
B 11
31.4%
T 11
31.4%
Space Separator
Value Count Frequency (%)
11
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 289
96.3%
Common 11
3.7%

Most frequent character per script

Latin
Value Count Frequency (%)
e 55
19.0%
i 37
12.8%
t 35
12.1%
n 24
8.3%
l 22
7.6%
K 13
4.5%
c 13
4.5%
h 13
4.5%
B 11
3.8%
r 11
3.8%
Other values (5) 55
19.0%
Common
Value Count Frequency (%)
11
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 300
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 55
18.3%
i 37
12.3%
t 35
11.7%
n 24
8.0%
l 22
7.3%
K 13
4.3%
c 13
4.3%
h 13
4.3%
B 11
3.7%
r 11
3.7%
Other values (6) 66
22.0%

fclass12
Categorical

HIGH CORRELATION
MISSING

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Distinct 2
Distinct (%) 8.3%
Missing 20723
Missing (%) 99.9%
Memory size 162.2 KiB
bar
13
restaurant
11

Length

Max length 10
Median length 3
Mean length 6.208333333
Min length 3

Characters and Unicode

Total characters 149
Distinct characters 8
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row restaurant
2nd row restaurant
3rd row restaurant
4th row restaurant
5th row restaurant

Common Values

Value Count Frequency (%)
bar 13
0.1%
restaurant 11
0.1%
(Missing) 20723
99.9%

Length

2022-07-04T20:06:04.059039 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:04.278708 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
bar 13
54.2%
restaurant 11
45.8%

Most occurring characters

Value Count Frequency (%)
a 35
23.5%
r 35
23.5%
t 22
14.8%
b 13
8.7%
e 11
7.4%
s 11
7.4%
u 11
7.4%
n 11
7.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 149
100.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 35
23.5%
r 35
23.5%
t 22
14.8%
b 13
8.7%
e 11
7.4%
s 11
7.4%
u 11
7.4%
n 11
7.4%

Most occurring scripts

Value Count Frequency (%)
Latin 149
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
a 35
23.5%
r 35
23.5%
t 22
14.8%
b 13
8.7%
e 11
7.4%
s 11
7.4%
u 11
7.4%
n 11
7.4%

Most occurring blocks

Value Count Frequency (%)
ASCII 149
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 35
23.5%
r 35
23.5%
t 22
14.8%
b 13
8.7%
e 11
7.4%
s 11
7.4%
u 11
7.4%
n 11
7.4%

code12
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Distinct 2
Distinct (%) 8.3%
Missing 20723
Missing (%) 99.9%
Memory size 162.2 KiB
2305.0
13
2301.0
11

Length

Max length 6
Median length 6
Mean length 6
Min length 6

Characters and Unicode

Total characters 144
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 2301.0
2nd row 2301.0
3rd row 2301.0
4th row 2301.0
5th row 2301.0

Common Values

Value Count Frequency (%)
2305.0 13
0.1%
2301.0 11
0.1%
(Missing) 20723
99.9%

Length

2022-07-04T20:06:04.454534 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:04.651170 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
2305.0 13
54.2%
2301.0 11
45.8%

Most occurring characters

Value Count Frequency (%)
0 48
33.3%
2 24
16.7%
3 24
16.7%
. 24
16.7%
5 13
9.0%
1 11
7.6%

Most occurring categories

Value Count Frequency (%)
Decimal Number 120
83.3%
Other Punctuation 24
16.7%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 48
40.0%
2 24
20.0%
3 24
20.0%
5 13
10.8%
1 11
9.2%
Other Punctuation
Value Count Frequency (%)
. 24
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 144
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 48
33.3%
2 24
16.7%
3 24
16.7%
. 24
16.7%
5 13
9.0%
1 11
7.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 144
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 48
33.3%
2 24
16.7%
3 24
16.7%
. 24
16.7%
5 13
9.0%
1 11
7.6%

name12
Categorical

HIGH CORRELATION
MISSING

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Distinct 2
Distinct (%) 8.3%
Missing 20723
Missing (%) 99.9%
Memory size 162.2 KiB
Simmons Rooms
13
Marktplatz
11

Length

Max length 13
Median length 13
Mean length 11.625
Min length 10

Characters and Unicode

Total characters 279
Distinct characters 16
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Marktplatz
2nd row Marktplatz
3rd row Marktplatz
4th row Marktplatz
5th row Marktplatz

Common Values

Value Count Frequency (%)
Simmons Rooms 13
0.1%
Marktplatz 11
0.1%
(Missing) 20723
99.9%

Length

2022-07-04T20:06:04.833296 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:05.054572 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
simmons 13
35.1%
rooms 13
35.1%
marktplatz 11
29.7%

Most occurring characters

Value Count Frequency (%)
m 39
14.0%
o 39
14.0%
s 26
9.3%
a 22
7.9%
t 22
7.9%
S 13
4.7%
i 13
4.7%
n 13
4.7%
13
4.7%
R 13
4.7%
Other values (6) 66
23.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 229
82.1%
Uppercase Letter 37
13.3%
Space Separator 13
4.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
m 39
17.0%
o 39
17.0%
s 26
11.4%
a 22
9.6%
t 22
9.6%
i 13
5.7%
n 13
5.7%
r 11
4.8%
k 11
4.8%
p 11
4.8%
Other values (2) 22
9.6%
Uppercase Letter
Value Count Frequency (%)
S 13
35.1%
R 13
35.1%
M 11
29.7%
Space Separator
Value Count Frequency (%)
13
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 266
95.3%
Common 13
4.7%

Most frequent character per script

Latin
Value Count Frequency (%)
m 39
14.7%
o 39
14.7%
s 26
9.8%
a 22
8.3%
t 22
8.3%
S 13
4.9%
i 13
4.9%
n 13
4.9%
R 13
4.9%
M 11
4.1%
Other values (5) 55
20.7%
Common
Value Count Frequency (%)
13
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 279
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
m 39
14.0%
o 39
14.0%
s 26
9.3%
a 22
7.9%
t 22
7.9%
S 13
4.7%
i 13
4.7%
n 13
4.7%
13
4.7%
R 13
4.7%
Other values (6) 66
23.7%

fclass13
Categorical

HIGH CORRELATION
MISSING

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Distinct 2
Distinct (%) 8.3%
Missing 20723
Missing (%) 99.9%
Memory size 162.2 KiB
hairdresser
13
jeweller
11

Length

Max length 11
Median length 11
Mean length 9.625
Min length 8

Characters and Unicode

Total characters 231
Distinct characters 10
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row jeweller
2nd row jeweller
3rd row jeweller
4th row jeweller
5th row jeweller

Common Values

Value Count Frequency (%)
hairdresser 13
0.1%
jeweller 11
0.1%
(Missing) 20723
99.9%

Length

2022-07-04T20:06:05.241074 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:05.458630 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
hairdresser 13
54.2%
jeweller 11
45.8%

Most occurring characters

Value Count Frequency (%)
e 59
25.5%
r 50
21.6%
s 26
11.3%
l 22
9.5%
h 13
5.6%
a 13
5.6%
i 13
5.6%
d 13
5.6%
j 11
4.8%
w 11
4.8%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 231
100.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 59
25.5%
r 50
21.6%
s 26
11.3%
l 22
9.5%
h 13
5.6%
a 13
5.6%
i 13
5.6%
d 13
5.6%
j 11
4.8%
w 11
4.8%

Most occurring scripts

Value Count Frequency (%)
Latin 231
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 59
25.5%
r 50
21.6%
s 26
11.3%
l 22
9.5%
h 13
5.6%
a 13
5.6%
i 13
5.6%
d 13
5.6%
j 11
4.8%
w 11
4.8%

Most occurring blocks

Value Count Frequency (%)
ASCII 231
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 59
25.5%
r 50
21.6%
s 26
11.3%
l 22
9.5%
h 13
5.6%
a 13
5.6%
i 13
5.6%
d 13
5.6%
j 11
4.8%
w 11
4.8%

code13
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Distinct 2
Distinct (%) 8.3%
Missing 20723
Missing (%) 99.9%
Memory size 162.2 KiB
2561.0
13
2520.0
11

Length

Max length 6
Median length 6
Mean length 6
Min length 6

Characters and Unicode

Total characters 144
Distinct characters 6
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 2520.0
2nd row 2520.0
3rd row 2520.0
4th row 2520.0
5th row 2520.0

Common Values

Value Count Frequency (%)
2561.0 13
0.1%
2520.0 11
0.1%
(Missing) 20723
99.9%

Length

2022-07-04T20:06:05.643332 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:05.858115 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
2561.0 13
54.2%
2520.0 11
45.8%

Most occurring characters

Value Count Frequency (%)
2 35
24.3%
0 35
24.3%
5 24
16.7%
. 24
16.7%
6 13
9.0%
1 13
9.0%

Most occurring categories

Value Count Frequency (%)
Decimal Number 120
83.3%
Other Punctuation 24
16.7%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
2 35
29.2%
0 35
29.2%
5 24
20.0%
6 13
10.8%
1 13
10.8%
Other Punctuation
Value Count Frequency (%)
. 24
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 144
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
2 35
24.3%
0 35
24.3%
5 24
16.7%
. 24
16.7%
6 13
9.0%
1 13
9.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 144
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
2 35
24.3%
0 35
24.3%
5 24
16.7%
. 24
16.7%
6 13
9.0%
1 13
9.0%

name13
Categorical

HIGH CORRELATION
MISSING

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Distinct 2
Distinct (%) 8.3%
Missing 20723
Missing (%) 99.9%
Memory size 162.2 KiB
Hair & Beauty Gallery
13
Goldschmied Gut
11

Length

Max length 21
Median length 21
Mean length 18.25
Min length 15

Characters and Unicode

Total characters 438
Distinct characters 19
Distinct categories 4 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Goldschmied Gut
2nd row Goldschmied Gut
3rd row Goldschmied Gut
4th row Goldschmied Gut
5th row Goldschmied Gut

Common Values

Value Count Frequency (%)
Hair & Beauty Gallery 13
0.1%
Goldschmied Gut 11
0.1%
(Missing) 20723
99.9%

Length

2022-07-04T20:06:06.048608 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:06.278665 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
hair 13
17.6%
13
17.6%
beauty 13
17.6%
gallery 13
17.6%
goldschmied 11
14.9%
gut 11
14.9%

Most occurring characters

Value Count Frequency (%)
50
11.4%
a 39
8.9%
l 37
8.4%
e 37
8.4%
G 35
8.0%
r 26
5.9%
y 26
5.9%
t 24
5.5%
i 24
5.5%
u 24
5.5%
Other values (9) 116
26.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 314
71.7%
Uppercase Letter 61
13.9%
Space Separator 50
11.4%
Other Punctuation 13
3.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 39
12.4%
l 37
11.8%
e 37
11.8%
r 26
8.3%
y 26
8.3%
t 24
7.6%
i 24
7.6%
u 24
7.6%
d 22
7.0%
o 11
3.5%
Other values (4) 44
14.0%
Uppercase Letter
Value Count Frequency (%)
G 35
57.4%
H 13
21.3%
B 13
21.3%
Space Separator
Value Count Frequency (%)
50
100.0%
Other Punctuation
Value Count Frequency (%)
& 13
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 375
85.6%
Common 63
14.4%

Most frequent character per script

Latin
Value Count Frequency (%)
a 39
10.4%
l 37
9.9%
e 37
9.9%
G 35
9.3%
r 26
6.9%
y 26
6.9%
t 24
6.4%
i 24
6.4%
u 24
6.4%
d 22
5.9%
Other values (7) 81
21.6%
Common
Value Count Frequency (%)
50
79.4%
& 13
20.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 438
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
50
11.4%
a 39
8.9%
l 37
8.4%
e 37
8.4%
G 35
8.0%
r 26
5.9%
y 26
5.9%
t 24
5.5%
i 24
5.5%
u 24
5.5%
Other values (9) 116
26.5%

fclass14
Categorical

HIGH CORRELATION
MISSING

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Distinct 2
Distinct (%) 8.3%
Missing 20723
Missing (%) 99.9%
Memory size 162.2 KiB
mobile_phone_shop
13
bookshop
11

Length

Max length 17
Median length 17
Mean length 12.875
Min length 8

Characters and Unicode

Total characters 309
Distinct characters 12
Distinct categories 2 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row bookshop
2nd row bookshop
3rd row bookshop
4th row bookshop
5th row bookshop

Common Values

Value Count Frequency (%)
mobile_phone_shop 13
0.1%
bookshop 11
0.1%
(Missing) 20723
99.9%

Length

2022-07-04T20:06:06.468939 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:06.675433 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
mobile_phone_shop 13
54.2%
bookshop 11
45.8%

Most occurring characters

Value Count Frequency (%)
o 72
23.3%
p 37
12.0%
h 37
12.0%
e 26
8.4%
_ 26
8.4%
b 24
7.8%
s 24
7.8%
m 13
4.2%
i 13
4.2%
l 13
4.2%
Other values (2) 24
7.8%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 283
91.6%
Connector Punctuation 26
8.4%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
o 72
25.4%
p 37
13.1%
h 37
13.1%
e 26
9.2%
b 24
8.5%
s 24
8.5%
m 13
4.6%
i 13
4.6%
l 13
4.6%
n 13
4.6%
Connector Punctuation
Value Count Frequency (%)
_ 26
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 283
91.6%
Common 26
8.4%

Most frequent character per script

Latin
Value Count Frequency (%)
o 72
25.4%
p 37
13.1%
h 37
13.1%
e 26
9.2%
b 24
8.5%
s 24
8.5%
m 13
4.6%
i 13
4.6%
l 13
4.6%
n 13
4.6%
Common
Value Count Frequency (%)
_ 26
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 309
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
o 72
23.3%
p 37
12.0%
h 37
12.0%
e 26
8.4%
_ 26
8.4%
b 24
7.8%
s 24
7.8%
m 13
4.2%
i 13
4.2%
l 13
4.2%
Other values (2) 24
7.8%

code14
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Distinct 2
Distinct (%) 8.3%
Missing 20723
Missing (%) 99.9%
Memory size 162.2 KiB
2525.0
13
2515.0
11

Length

Max length 6
Median length 6
Mean length 6
Min length 6

Characters and Unicode

Total characters 144
Distinct characters 5
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 2515.0
2nd row 2515.0
3rd row 2515.0
4th row 2515.0
5th row 2515.0

Common Values

Value Count Frequency (%)
2525.0 13
0.1%
2515.0 11
0.1%
(Missing) 20723
99.9%

Length

2022-07-04T20:06:06.866477 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:07.071669 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
2525.0 13
54.2%
2515.0 11
45.8%

Most occurring characters

Value Count Frequency (%)
5 48
33.3%
2 37
25.7%
. 24
16.7%
0 24
16.7%
1 11
7.6%

Most occurring categories

Value Count Frequency (%)
Decimal Number 120
83.3%
Other Punctuation 24
16.7%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
5 48
40.0%
2 37
30.8%
0 24
20.0%
1 11
9.2%
Other Punctuation
Value Count Frequency (%)
. 24
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 144
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
5 48
33.3%
2 37
25.7%
. 24
16.7%
0 24
16.7%
1 11
7.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 144
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
5 48
33.3%
2 37
25.7%
. 24
16.7%
0 24
16.7%
1 11
7.6%

name14
Categorical

HIGH CORRELATION
MISSING

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Distinct 2
Distinct (%) 8.3%
Missing 20723
Missing (%) 99.9%
Memory size 162.2 KiB
IPB Technology
13
Rösslitor Bücher
11

Length

Max length 16
Median length 14
Mean length 14.91666667
Min length 14

Characters and Unicode

Total characters 358
Distinct characters 20
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Rösslitor Bücher
2nd row Rösslitor Bücher
3rd row Rösslitor Bücher
4th row Rösslitor Bücher
5th row Rösslitor Bücher

Common Values

Value Count Frequency (%)
IPB Technology 13
0.1%
Rösslitor Bücher 11
0.1%
(Missing) 20723
99.9%

Length

2022-07-04T20:06:07.510536 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:07.754213 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
ipb 13
27.1%
technology 13
27.1%
rösslitor 11
22.9%
bücher 11
22.9%

Most occurring characters

Value Count Frequency (%)
o 37
10.3%
l 24
6.7%
B 24
6.7%
24
6.7%
e 24
6.7%
c 24
6.7%
h 24
6.7%
r 22
6.1%
s 22
6.1%
g 13
3.6%
Other values (10) 120
33.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 260
72.6%
Uppercase Letter 74
20.7%
Space Separator 24
6.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
o 37
14.2%
l 24
9.2%
e 24
9.2%
c 24
9.2%
h 24
9.2%
r 22
8.5%
s 22
8.5%
g 13
5.0%
y 13
5.0%
n 13
5.0%
Other values (4) 44
16.9%
Uppercase Letter
Value Count Frequency (%)
B 24
32.4%
I 13
17.6%
P 13
17.6%
T 13
17.6%
R 11
14.9%
Space Separator
Value Count Frequency (%)
24
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 334
93.3%
Common 24
6.7%

Most frequent character per script

Latin
Value Count Frequency (%)
o 37
11.1%
l 24
7.2%
B 24
7.2%
e 24
7.2%
c 24
7.2%
h 24
7.2%
r 22
6.6%
s 22
6.6%
g 13
3.9%
y 13
3.9%
Other values (9) 107
32.0%
Common
Value Count Frequency (%)
24
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 336
93.9%
None 22
6.1%

Most frequent character per block

ASCII
Value Count Frequency (%)
o 37
11.0%
l 24
7.1%
B 24
7.1%
24
7.1%
e 24
7.1%
c 24
7.1%
h 24
7.1%
r 22
6.5%
s 22
6.5%
g 13
3.9%
Other values (8) 98
29.2%
None
Value Count Frequency (%)
ö 11
50.0%
ü 11
50.0%

fclass15
Categorical

HIGH CORRELATION
MISSING

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Distinct 2
Distinct (%) 8.3%
Missing 20723
Missing (%) 99.9%
Memory size 162.2 KiB
dentist
13
restaurant
11

Length

Max length 10
Median length 7
Mean length 8.375
Min length 7

Characters and Unicode

Total characters 201
Distinct characters 9
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row restaurant
2nd row restaurant
3rd row restaurant
4th row restaurant
5th row restaurant

Common Values

Value Count Frequency (%)
dentist 13
0.1%
restaurant 11
0.1%
(Missing) 20723
99.9%

Length

2022-07-04T20:06:07.960368 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:08.183027 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
dentist 13
54.2%
restaurant 11
45.8%

Most occurring characters

Value Count Frequency (%)
t 48
23.9%
e 24
11.9%
n 24
11.9%
s 24
11.9%
r 22
10.9%
a 22
10.9%
d 13
6.5%
i 13
6.5%
u 11
5.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 201
100.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
t 48
23.9%
e 24
11.9%
n 24
11.9%
s 24
11.9%
r 22
10.9%
a 22
10.9%
d 13
6.5%
i 13
6.5%
u 11
5.5%

Most occurring scripts

Value Count Frequency (%)
Latin 201
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
t 48
23.9%
e 24
11.9%
n 24
11.9%
s 24
11.9%
r 22
10.9%
a 22
10.9%
d 13
6.5%
i 13
6.5%
u 11
5.5%

Most occurring blocks

Value Count Frequency (%)
ASCII 201
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
t 48
23.9%
e 24
11.9%
n 24
11.9%
s 24
11.9%
r 22
10.9%
a 22
10.9%
d 13
6.5%
i 13
6.5%
u 11
5.5%

code15
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Distinct 2
Distinct (%) 8.3%
Missing 20723
Missing (%) 99.9%
Memory size 162.2 KiB
2121.0
13
2301.0
11

Length

Max length 6
Median length 6
Mean length 6
Min length 6

Characters and Unicode

Total characters 144
Distinct characters 5
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 2301.0
2nd row 2301.0
3rd row 2301.0
4th row 2301.0
5th row 2301.0

Common Values

Value Count Frequency (%)
2121.0 13
0.1%
2301.0 11
0.1%
(Missing) 20723
99.9%

Length

2022-07-04T20:06:08.361004 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:08.568361 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
2121.0 13
54.2%
2301.0 11
45.8%

Most occurring characters

Value Count Frequency (%)
2 37
25.7%
1 37
25.7%
0 35
24.3%
. 24
16.7%
3 11
7.6%

Most occurring categories

Value Count Frequency (%)
Decimal Number 120
83.3%
Other Punctuation 24
16.7%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
2 37
30.8%
1 37
30.8%
0 35
29.2%
3 11
9.2%
Other Punctuation
Value Count Frequency (%)
. 24
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 144
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
2 37
25.7%
1 37
25.7%
0 35
24.3%
. 24
16.7%
3 11
7.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 144
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
2 37
25.7%
1 37
25.7%
0 35
24.3%
. 24
16.7%
3 11
7.6%

name15
Categorical

HIGH CORRELATION
MISSING

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Distinct 2
Distinct (%) 8.3%
Missing 20723
Missing (%) 99.9%
Memory size 162.2 KiB
LDNDental
13
schiffchuchi
11

Length

Max length 12
Median length 9
Mean length 10.375
Min length 9

Characters and Unicode

Total characters 249
Distinct characters 14
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row schiffchuchi
2nd row schiffchuchi
3rd row schiffchuchi
4th row schiffchuchi
5th row schiffchuchi

Common Values

Value Count Frequency (%)
LDNDental 13
0.1%
schiffchuchi 11
0.1%
(Missing) 20723
99.9%

Length

2022-07-04T20:06:08.759371 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:06:08.989743 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
ldndental 13
54.2%
schiffchuchi 11
45.8%

Most occurring characters

Value Count Frequency (%)
c 33
13.3%
h 33
13.3%
D 26
10.4%
i 22
8.8%
f 22
8.8%
L 13
5.2%
N 13
5.2%
e 13
5.2%
n 13
5.2%
t 13
5.2%
Other values (4) 48
19.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 197
79.1%
Uppercase Letter 52
20.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
c 33
16.8%
h 33
16.8%
i 22
11.2%
f 22
11.2%
e 13
6.6%
n 13
6.6%
t 13
6.6%
a 13
6.6%
l 13
6.6%
s 11
5.6%
Uppercase Letter
Value Count Frequency (%)
D 26
50.0%
L 13
25.0%
N 13
25.0%

Most occurring scripts

Value Count Frequency (%)
Latin 249
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
c 33
13.3%
h 33
13.3%
D 26
10.4%
i 22
8.8%
f 22
8.8%
L 13
5.2%
N 13
5.2%
e 13
5.2%
n 13
5.2%
t 13
5.2%
Other values (4) 48
19.3%

Most occurring blocks

Value Count Frequency (%)
ASCII 249
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
c 33
13.3%
h 33
13.3%
D 26
10.4%
i 22
8.8%
f 22
8.8%
L 13
5.2%
N 13
5.2%
e 13
5.2%
n 13
5.2%
t 13
5.2%
Other values (4) 48
19.3%

fclass16
Unsupported

MISSING
REJECTED
UNSUPPORTED

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Missing 20747
Missing (%) 100.0%
Memory size 162.2 KiB

code16
Unsupported

MISSING
REJECTED
UNSUPPORTED

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Missing 20747
Missing (%) 100.0%
Memory size 162.2 KiB

name16
Unsupported

MISSING
REJECTED
UNSUPPORTED

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Missing 20747
Missing (%) 100.0%
Memory size 162.2 KiB

fclass17
Unsupported

MISSING
REJECTED
UNSUPPORTED

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Missing 20747
Missing (%) 100.0%
Memory size 162.2 KiB

code17
Unsupported

MISSING
REJECTED
UNSUPPORTED

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Missing 20747
Missing (%) 100.0%
Memory size 162.2 KiB

name17
Unsupported

MISSING
REJECTED
UNSUPPORTED

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Missing 20747
Missing (%) 100.0%
Memory size 162.2 KiB

fclass18
Unsupported

MISSING
REJECTED
UNSUPPORTED

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Missing 20747
Missing (%) 100.0%
Memory size 162.2 KiB

code18
Unsupported

MISSING
REJECTED
UNSUPPORTED

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Missing 20747
Missing (%) 100.0%
Memory size 162.2 KiB

name18
Unsupported

MISSING
REJECTED
UNSUPPORTED

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Missing 20747
Missing (%) 100.0%
Memory size 162.2 KiB

fclass19
Unsupported

MISSING
REJECTED
UNSUPPORTED

Class name of this feature. This does not add any information that is not already in the “code” field, but it is better readable. There is one for each POI near the user's location

Missing 20747
Missing (%) 100.0%
Memory size 162.2 KiB

code19
Unsupported

MISSING
REJECTED
UNSUPPORTED

4-digit code (between 1000 and 9999) defining the feature class. The first one or two digits define the layer, the last two or three digits the class inside a layer. There is one for each POI near the user's location

Missing 20747
Missing (%) 100.0%
Memory size 162.2 KiB

name19
Unsupported

MISSING
REJECTED
UNSUPPORTED

Name of this feature, like a street or place name. If the name in OSM contains obviously wrong data such as “fixme” or “none”, it will be empty. There is one for each POI near the user's location

Missing 20747
Missing (%) 100.0%
Memory size 162.2 KiB

Interactions

2022-07-04T20:05:30.298629 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:16.948182 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:18.783165 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:20.681664 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:22.698896 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:24.481621 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:26.363526 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:28.522489 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:30.498792 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:17.193021 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:19.043381 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:20.908843 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:22.921303 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:24.713147 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:26.576768 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:28.750182 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:30.689961 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:17.444256 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:19.298599 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:21.153492 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:23.142823 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:24.945358 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:26.788146 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:28.967834 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:30.893022 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:17.666527 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:19.532632 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:21.597261 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:23.379004 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:25.188854 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:27.033039 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:29.202139 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:31.098320 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:17.901541 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:19.780250 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:21.825701 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:23.605516 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:25.419356 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:27.271567 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:29.417193 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:31.320703 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:18.135957 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:20.029539 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:22.052733 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:23.833374 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:25.663287 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:27.509178 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:29.637046 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:31.528335 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:18.356296 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:20.246842 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:22.263617 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:24.050004 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:25.902915 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:27.739941 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:29.857882 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:31.741393 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:18.577037 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:20.474057 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:22.487116 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:24.269307 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:26.143123 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:28.298418 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:05:30.076609 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-04T20:06:09.241372 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient ( ρ ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r . It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y , one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-04T20:06:09.922409 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient ( r ) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r .

To calculate r for two variables X and Y , one divides the covariance of X and Y by the product of their standard deviations.
2022-07-04T20:06:10.593724 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient ( τ ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y , one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-04T20:06:11.358427 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here .

Missing values

2022-07-04T20:05:32.807581 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-07-04T20:05:36.747846 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-07-04T20:05:41.713711 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.